CN111472750B - Well logging curve interpretation method and device - Google Patents
Well logging curve interpretation method and device Download PDFInfo
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Abstract
The invention discloses a log interpretation method and a log interpretation device, wherein the method comprises the following steps: and respectively reconstructing the value range of the obtained longitudinal wave velocity log curve and the density log curve of the target reservoir, filtering the density log curve after reconstructing the value range to obtain compaction trend information of the target reservoir, subtracting the compaction trend information of the target reservoir from the longitudinal wave velocity log curve after reconstructing the value range to obtain a high-frequency velocity log curve, determining a fluid characteristic curve according to the high-frequency velocity log curve, and further determining the distribution of pore fluid of the target reservoir. The direct current component of the density logging curve shows compaction trend information, the fluid characteristic curve shows speed dispersion characteristics, the longitudinal wave speed logging curve after the value range is reconstructed subtracts the compaction trend information of the target reservoir to obtain a high-frequency speed logging curve, and then the fluid characteristic curve of the target reservoir is determined, so that the accuracy and precision for predicting the pore fluid distribution of the target reservoir can be improved.
Description
Technical Field
The invention relates to the technical field of petroleum geophysical exploration, in particular to a well logging curve interpretation method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
In the petroleum exploration and development process, a logging curve reveals various physical characteristics of formations surrounding a well bore, and is a geophysical measurement method directly reflecting reservoir characteristics. In general, conventional well logging curves include acoustic logging curves, density logging curves, natural gamma logging curves (i.e., GR curves), natural potential logging curves (i.e., SR curves), various resistivity curves, well diameter curves, and porosity curves, which reflect information about lithology, physical properties, and fluid-containing properties of a reservoir, respectively. With the continuous deep development of petroleum exploration, the combination of the seismic data and the well logging curve is tighter, some original characteristic technologies based on the seismic data are beginning to be applied to the well logging curve interpretation field, the seismic data reflect the physical characteristics of underground media, and the acoustic wave and density well logging curve in the well logging curve and the seismic data have physical consistency, and the difference between the well logging curve and the seismic data is the difference of research scales.
The rock physical theory research shows that the aspect ratio attribute can effectively judge the fluid-containing property of the reservoir, so that the pre-stack seismic inversion is more and more emphasized, the longitudinal-transverse wave velocity ratio and the longitudinal wave impedance are obtained through the pre-stack seismic inversion, and the distribution of reservoir pore fluid can be judged by joint application. However, this technique is not fully utilized in the field of log interpretation, and the root cause is the lack or unreliability of the shear wave log. The transverse wave prediction at the present stage is mainly obtained through calculation of a physical model of theoretical rock, the reliability degree of a prediction result is seriously dependent on the rationality of the physical model and the accuracy of basic parameters, and the transverse wave information predicted by the theoretical model often cannot meet the requirements due to the reasons of complex and changeable underground medium, strong non-uniformity and the like, so that the accuracy and the precision of predicting the distribution of reservoir pore fluid are lower.
In the field of geophysics, seismic velocity is characterized by dispersion, i.e., the velocity of the seismic changes as the frequency changes, and the cause of such changes is the presence of fluid flow in the reservoir. Thus, the seismic velocity dispersion property is also considered to be an effective measure of reservoir fluid-containing properties. As the seismic wave propagates through the fluid-containing rock, the high frequency information is rapidly attenuated, the energy is attenuated, and the low frequency information energy is substantially maintained. Likewise, this physical phenomenon also exists for velocity logs obtained from sonic logging, and thus utilizing the velocity dispersion properties of velocity logs based on sonic logging can help predict reservoir fluid-containing properties around the wellbore. However, the velocity log records compaction trend information of the reservoir during the measurement process, and the amplitude range of such compaction trend information is generally wide, so that a submerged effect is achieved on the fine velocity dispersion characteristics, and the accuracy and precision of predicting the distribution of the reservoir pore fluid are also low.
Thus, existing log interpretation has the problem of lower accuracy and precision in predicting the distribution of reservoir pore fluids.
Disclosure of Invention
The embodiment of the invention provides a log interpretation method for improving accuracy and precision of predicting reservoir pore fluid distribution, comprising the following steps of:
acquiring a longitudinal wave velocity log curve and a density log curve of a target reservoir;
reconstructing the value range of the longitudinal wave velocity log curve and the density log curve of the target reservoir respectively to obtain the longitudinal wave velocity log curve after reconstructing the value range and the density log curve after reconstructing the value range;
long wavelength filtering is carried out on the density logging curve after the reconstruction value range, and compaction trend information of the target reservoir is obtained;
subtracting compaction trend information of the target reservoir from the longitudinal wave velocity log after the value range is reconstructed to obtain a high-frequency velocity log of the target reservoir;
determining a fluid characteristic curve of the target reservoir according to the high-frequency speed logging curve;
and determining the distribution of the pore fluid of the target reservoir according to the fluid characteristic curve of the target reservoir.
The embodiment of the invention also provides a log interpretation device for improving the accuracy and precision of predicting the pore fluid distribution of a reservoir, which comprises the following steps:
The acquisition module is used for acquiring a longitudinal wave velocity log curve and a density log curve of the target reservoir;
the value range reconstruction module is used for reconstructing the value range of the longitudinal wave velocity log curve and the density log curve of the target reservoir respectively to obtain the longitudinal wave velocity log curve after the value range is reconstructed and the density log curve after the value range is reconstructed;
the filtering module is used for carrying out long-wavelength filtering on the density logging curve after the reconstruction value range to obtain compaction trend information of the target reservoir;
the high-frequency velocity logging curve acquisition module is used for subtracting compaction trend information of the target reservoir from the longitudinal wave velocity logging curve after the value range is reconstructed to obtain the high-frequency velocity logging curve of the target reservoir;
the fluid characteristic curve determining module is used for determining a fluid characteristic curve of the target reservoir according to the high-frequency speed logging curve;
and the pore fluid distribution determining module is used for determining the distribution of the pore fluid of the target reservoir according to the fluid characteristic curve of the target reservoir.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the well logging interpretation method when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program for executing the above-described log interpretation method.
In the embodiment of the invention, a longitudinal wave velocity log curve and a density log curve of a target reservoir are obtained, the longitudinal wave velocity log curve and the density log curve of the target reservoir are respectively subjected to value domain reconstruction, the longitudinal wave velocity log curve after the value domain reconstruction and the density log curve after the value domain reconstruction are obtained, long wavelength filtering is carried out on the density log curve after the value domain reconstruction, compaction trend information of the target reservoir is obtained, the longitudinal wave velocity log curve after the value domain reconstruction is utilized to subtract the compaction trend information of the target reservoir, a high-frequency velocity log curve of the target reservoir is obtained, a fluid characteristic curve of the target reservoir is determined according to the high-frequency velocity log curve, and the distribution of pore fluid of the target reservoir is determined according to the fluid characteristic curve of the target reservoir. According to the embodiment of the invention, under the condition that the transverse wave logging curve is missing, the compaction trend information of the target reservoir is subtracted from the longitudinal wave velocity logging curve after the value range is reconstructed, the high-frequency velocity logging curve of the target reservoir is obtained, the fluid characteristic curve of the target reservoir is further determined according to the high-frequency velocity logging curve, the direct current component of the density logging curve shows the compaction trend information of the target reservoir, the fluid characteristic curve shows the velocity dispersion characteristic of the target reservoir, and the fluid characteristic curve after the compaction trend information of the target reservoir is removed can improve the accuracy and precision of predicting the pore fluid distribution of the target reservoir.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart illustrating an implementation of a log interpretation method provided by an embodiment of the present invention;
FIG. 2 is a functional block diagram of a log interpretation apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a log of longitudinal wave velocity after an actual reservoir reconstruction range provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of a density log after an actual reservoir reconstruction range provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of the DC component of a density log after an actual reservoir reconstruction range provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of the DC component of an actual reservoir high frequency velocity log provided by an embodiment of the present invention;
fig. 7 is a schematic diagram of an actual reservoir fluid characteristic curve provided by an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
Although the invention provides a method operation or apparatus structure as shown in the following examples or figures, more or fewer operation steps or module units may be included in the method or apparatus based on routine or non-inventive labor. In the steps or the structures of the apparatuses in which there is no necessary cause and effect logically, the execution order of the steps or the structure of the modules is not limited to the execution order or the structure of the modules shown in the embodiments or the drawings of the present invention. The method or module structure described may be performed sequentially or in parallel according to the embodiment or the method or module structure shown in the drawings when applied to a device or an end product in practice.
Aiming at the defect that the accuracy and precision of predicting reservoir pore fluid distribution in the log interpretation in the prior art are low, the applicant of the invention provides a log interpretation method and device. In view of the fact that the direct current component of the density logging curve shows compaction trend information of the target reservoir, the fluid characteristic curve shows speed dispersion characteristics of the target reservoir, and the purpose of improving accuracy and precision of predicting pore fluid distribution of the target reservoir can be achieved after the fluid characteristic curve of the compaction trend information of the target reservoir is removed.
Fig. 1 shows a flow of implementation of a log interpretation method according to an embodiment of the present invention, and for convenience of description, only a portion relevant to the embodiment of the present invention is shown, which is described in detail below:
as shown in fig. 1, the log interpretation method includes:
102, reconstructing a value range of a longitudinal wave velocity log curve and a density log curve of a target reservoir respectively to obtain the longitudinal wave velocity log curve after the value range is reconstructed and the density log curve after the value range is reconstructed;
and 106, determining the distribution of the pore fluid of the target reservoir according to the fluid characteristic curve of the target reservoir.
The target reservoir is a reservoir where a regular distribution of pores in the reservoir needs to be determined. In order to achieve the purpose of predicting the distribution of pore fluid of a target reservoir, a longitudinal wave velocity log and a density log of the target reservoir are first obtained for the target reservoir.
Sonic velocity logging refers to a logging method in a borehole to determine properties of a target reservoir by studying the propagation velocity of sound waves in the target reservoir. The acoustic wave velocity includes both shear wave velocity and longitudinal wave velocity. The longitudinal wave velocity reflects the longitudinal wave velocity attribute characteristics of the target reservoir, and the corresponding log is referred to as the longitudinal wave velocity log of the target reservoir.
The density logging curve is an important logging method for determining lithology and rock density of a target reservoir, and is combined with acoustic logging and neutron logging to form lithology porosity logging series. The density reflects the density attribute characteristics of the target reservoir, and the corresponding log is referred to as the density log of the target reservoir.
In a normal compaction section of the reservoir, the relation that the formation porosity of the target reservoir changes along with the formation burial depth reflects the formation compaction rule, namely the compaction trend of the formation. In an embodiment of the invention, the density log includes compaction trend information for the target reservoir. And the compaction trend information is removed from the longitudinal wave speed logging curve, so that the distribution of the pore fluid of the target reservoir can be determined more accurately.
In view of the difference between the measurement of the longitudinal wave velocity and the measurement of the density represented by the longitudinal wave velocity log and the measurement of the density log, the value range of the longitudinal wave velocity log and the measurement of the density log of the target reservoir are required to be reconstructed respectively, so that the measurement of the longitudinal wave velocity and the measurement of the density represented by the longitudinal wave velocity log and the measurement of the density log of the target reservoir are unified, and the longitudinal wave velocity log and the measurement of the density log after the value range is reconstructed are obtained.
In one embodiment of the present invention, the range of the longitudinal wave velocity log after reconstructing the range and the density log after reconstructing the range is between 0 and 1. In the embodiment, the longitudinal wave velocity log and the density log are normalized respectively so that the measurement of the longitudinal wave velocity and the density represented by the normalized longitudinal wave velocity log and the normalized density log are consistent.
After the density logging curve with the reconstructed value range is obtained, long wavelength filtering is carried out on the density logging curve with the reconstructed value range, so that compaction trend information of a target reservoir is obtained, and the compaction trend information of the target reservoir is the direct current component of the density logging curve and is characterized as the compaction trend information of the target reservoir. And the direct current component of the density logging curve after the reconstruction value range is subtracted from the longitudinal wave velocity logging curve after the reconstruction value range is utilized, compaction trend information is removed from the longitudinal wave velocity logging curve, a high-frequency velocity logging curve of the target reservoir is obtained, and then the distribution of the pore fluid of the target reservoir is predicted according to the high-frequency velocity logging curve, and finally the distribution of the pore fluid of the target reservoir is determined.
When the distribution of the pore fluid of the target reservoir is determined according to the high-frequency speed logging curve, the fluid characteristic curve of the target reservoir is determined according to the high-frequency speed logging curve, the high-frequency speed attribute characteristic of the target reservoir is converted into the fluid attribute characteristic of the target reservoir, and then the distribution of the pore fluid of the target reservoir is determined according to the fluid characteristic curve of the target reservoir, so that the prediction of the pore fluid distribution rule of the target reservoir is realized.
The fluid characteristic curve reflects the trend of change in the water saturation of the target reservoir, and when the distribution of the pore fluid of the target reservoir is determined according to the fluid characteristic curve of the target reservoir, the larger the fluid characteristic curve value is, the smaller the water saturation (generally considered as the water saturation not to be more than 20%) and the more the hydrocarbon is in the pores at the position corresponding to the fluid characteristic curve value in the target reservoir are, which indicates that the position corresponding to the fluid characteristic curve value is likely to be a hydrocarbon reservoir, namely, the presence of a hydrocarbon reservoir is indicated.
The smaller the fluid profile value, the greater the water saturation (generally considered to be no less than 80%) and the less hydrocarbon in the pores at the location in the target reservoir corresponding to the fluid profile value, indicating that the location in the fluid profile value is likely to be the water layer, i.e., indicating the presence of the water layer.
In the embodiment of the invention, a longitudinal wave velocity log curve and a density log curve of a target reservoir are obtained, the longitudinal wave velocity log curve and the density log curve of the target reservoir are respectively subjected to value range reconstruction, the longitudinal wave velocity log curve after the value range reconstruction and the density log curve after the value range reconstruction are obtained, long wavelength filtering is carried out on the density log curve after the value range reconstruction, compaction trend information of the target reservoir is obtained, the longitudinal wave velocity log curve after the value range reconstruction is used for subtracting the compaction trend information of the target reservoir, a high-frequency velocity log curve of the target reservoir is obtained, a fluid characteristic curve of the target reservoir is determined according to the high-frequency velocity log curve, and the distribution of pore fluid of the target reservoir is determined according to the fluid characteristic curve of the target reservoir. According to the embodiment of the invention, under the condition that the transverse wave logging curve is missing, the compaction trend information of the target reservoir is subtracted from the longitudinal wave velocity logging curve after the value range is reconstructed, the high-frequency velocity logging curve of the target reservoir is obtained, the fluid characteristic curve of the target reservoir is further determined according to the high-frequency velocity logging curve, the direct current component of the density logging curve shows the compaction trend information of the target reservoir, the fluid characteristic curve shows the velocity dispersion characteristic of the target reservoir, and the fluid characteristic curve after the compaction trend information of the target reservoir is removed can improve the accuracy and precision of predicting the pore fluid distribution of the target reservoir.
In an embodiment of the present invention, in order to further improve accuracy and precision of predicting a pore fluid distribution of a target reservoir, in a log interpretation method, step 102, reconstructing a value range of a longitudinal wave velocity log and a density log of the target reservoir, to obtain the reconstructed value range of the longitudinal wave velocity log and the reconstructed value range of the density log, includes:
determining the maximum value and the minimum value of a longitudinal wave velocity logging curve;
and step, reconstructing the value range of the longitudinal wave velocity logging curve of the target reservoir according to the longitudinal wave velocity logging curve, the maximum value and the minimum value of the longitudinal wave velocity logging curve, and obtaining the longitudinal wave velocity logging curve after reconstructing the value range.
When reconstructing the value range of the longitudinal wave velocity log, firstly determining the maximum value V of the longitudinal wave velocity log max And a minimum value V min Further according to the longitudinal wave velocity logging curve and its maximum value V max And a minimum value V min And reconstructing the value range of the longitudinal wave velocity log curve of the target reservoir, and further obtaining the longitudinal wave velocity log curve after reconstructing the value range.
The value range of the longitudinal wave velocity log curve can be reconstructed by the following formula:
V 2 (j)＝[V 1 (j)-V min ]/[V max -V min ]；
wherein V is 2 (j) Representing a longitudinal wave velocity log after reconstructing the value range, V 1 (j) Representing a longitudinal wave velocity log before value domain reconstruction, j representing a sampling point of the longitudinal wave velocity log, V max Before reconstruction of representation value rangeMaximum value of longitudinal wave velocity log, V min Representing the minimum value of the longitudinal wave velocity log before the value range reconstruction.
In the embodiment of the invention, the maximum value and the minimum value of the longitudinal wave velocity logging curve are determined, the value range of the longitudinal wave velocity logging curve of the target reservoir is reconstructed according to the longitudinal wave velocity logging curve and the maximum value and the minimum value thereof, the longitudinal wave velocity logging curve after the value range is reconstructed is obtained, and the accuracy and the precision for predicting the pore fluid distribution of the target reservoir can be further improved.
In an embodiment of the present invention, in order to further improve accuracy and precision of predicting a pore fluid distribution of a target reservoir, in a log interpretation method, step 102, reconstructing a value range of a longitudinal wave velocity log and a density log of the target reservoir, to obtain the reconstructed value range of the longitudinal wave velocity log and the reconstructed value range of the density log, includes:
determining the maximum value and the minimum value of the density logging curve;
and reconstructing the value range of the density logging curve of the target reservoir according to the density logging curve and the maximum value and the minimum value of the density logging curve, and obtaining the density logging curve after reconstructing the value range.
In reconstructing the value range of the density log, the maximum ρ of the density log is first determined max And a minimum value ρ min And then reconstructing the value range of the density logging curve of the target reservoir according to the density logging curve and the maximum value and the minimum value of the density logging curve, and further obtaining the density logging curve after reconstructing the value range.
The value range of the density logging curve can be reconstructed by the following formula:
ρ 2 (j)＝[ρ 1 (j)-ρ min ]/[ρ max -ρ min ]；
wherein ρ is 2 (j) Representing the density log after reconstructing the value range ρ 1 (j) Representing the density log before value range reconstruction, j representing the sampling point of the density log, ρ max Representing the maximum value of a density log prior to value range reconstruction，ρ min Representing the minimum of the density log before the value range reconstruction.
In the embodiment of the invention, the maximum value and the minimum value of the density logging curve are determined, the value range of the density logging curve of the target reservoir is reconstructed according to the density logging curve and the maximum value and the minimum value of the density logging curve, the density logging curve after the value range is reconstructed is obtained, and the accuracy and the precision of predicting the pore fluid distribution of the target reservoir can be further improved.
In an embodiment of the present invention, in order to further improve accuracy and precision of predicting pore fluid distribution of a target reservoir, in step 103 of the log interpretation method, long wavelength filtering is performed on a density log after reconstruction of a value range to obtain compaction trend information of the target reservoir, including:
Constructing a filter and determining a filtering operator of the filter;
and step, long wavelength filtering is carried out on the density logging curve after the reconstruction value range by using a filtering operator of the filter, so as to obtain compaction trend information of the target reservoir.
In order to determine compaction trend information for a target reservoir, long wavelength filtering of the reconstructed value range density log using a filter is required. Wherein the long wavelength range includes 1310 nm to 1550 nm wavelengths. Thus, a filter is constructed, and the filter operator of the filter is determined. Wherein the filter is a long wavelength filter. The filtering operators of the finally determined filter are as follows:
wherein T (j) is a filtering operator, G (j) is the distance from the sampling point j to the center of the frequency spectrum, and G 0 For the passband radius of the filter, n represents the power coefficient of the filter, which determines the shape of the filter, the position of the spectral center is
In one embodiment of the invention, the pass band radius G of the filter 0 The power coefficient of the filter n=2, the position of the spectral center is (0,150), i.e. m=300, =10.
After a filter is constructed and a filtering operator of the filter is determined, long-wavelength filtering is carried out on the density logging curve after the reconstruction value range by using the filtering operator of the filter, and compaction trend information of the target reservoir is obtained. Specifically, long wavelength filtering can be performed on the density log after the reconstruction range by the following formula:
D(j)＝ρ 2 (j)×T(j)；
Wherein D (j) represents the DC component of the density log after reconstruction of the value range, which characterizes compaction trend information for the target reservoir, ρ 2 (j) And (3) reconstructing a density logging curve after the value range, wherein T (j) is a filtering operator of the filter.
In the embodiment of the invention, a filter is constructed, a filter operator of the filter is determined, long-wavelength filtering is carried out on the density logging curve after the reconstruction value range by using the filter operator of the filter, compaction trend information of the target reservoir is obtained, and the accuracy and precision of predicting the pore fluid distribution of the target reservoir can be further improved.
And after the compaction trend information of the target reservoir is determined, namely, reconstructing the direct current component D (j) of the density logging curve after the value range, and subtracting the compaction trend information of the target reservoir from the longitudinal wave velocity logging curve after the value range is reconstructed to obtain the high-frequency velocity logging curve of the target reservoir. Specifically, the high frequency velocity log of the target reservoir may be determined by the following formula:
V 3 (j)＝V 2 (j)-D(j)；
wherein V is 3 (j) To remove compaction trend information of high frequency velocity log of target reservoir, V 2 (j) Representing the longitudinal wave velocity log after the reconstruction range, and D (j) represents the dc component of the density log after the reconstruction range.
In one embodiment of the present invention, to further improve accuracy and precision of predicting a target reservoir pore fluid distribution, step 105 of the log interpretation method, determining a fluid characteristic curve of the target reservoir from the high frequency velocity log comprises:
step, carrying out Laplacian transformation on the high-frequency speed logging curve to obtain speed logging curves corresponding to different frequencies;
and determining a fluid characteristic curve of the target reservoir according to the speed logging curves corresponding to different frequencies.
The laplace transform, also known as the laplace transform (S transform), is a linear integral transform. In the embodiment of the invention, laplacian transformation is applied to the high-frequency speed logging curve to obtain the speed logging curve corresponding to different frequencies. Specifically, the Laplacian transformation can be performed on the high-frequency speed logging curve through the following formula:
wherein V is 3 (j, f) represents velocity logs corresponding to different frequencies, x represents depth V at a location in the target reservoir 3 (x) Representing the value of the velocity profile corresponding to the depth x, lambda a Represents the adjustment factor of the laplace transform, p represents the adjustment power coefficient of the laplace transform, and f represents the frequency.
In one embodiment of the invention, the adjustment factor lambda a =1.2, adjusting the power coefficient p=1.
After obtaining the velocity log curves corresponding to different frequencies, the fluid characteristic curve of the target reservoir can be determined specifically by the following formula:
order the
Then according to the formula of Laplace transformation on the high-frequency speed logging curve, the first-order Taylor formula expansion is carried out on the high-frequency speed logging curve, so that the method can be obtained:
wherein A represents the fluid profile of the target reservoir, V 3 (j, f) represents velocity log curves corresponding to different frequencies, V 3 (j,f 0 ) Representing a velocity log corresponding to a reference frequency, f representing frequency, f 0 Representing the reference frequency.
In one embodiment of the present invention, the reference frequency f 0 ＝5Hz。
And further, the distribution of the pore fluid of the target reservoir can be determined according to the obtained fluid characteristic curve of the target reservoir.
In the embodiment of the invention, the Laplace transformation is carried out on the high-frequency velocity logging curves to obtain the velocity logging curves corresponding to different frequencies, and the fluid characteristic curves of the target reservoir are determined according to the velocity logging curves corresponding to different frequencies, so that the accuracy and precision of predicting the pore fluid distribution of the target reservoir can be further improved.
The embodiment of the invention also provides a log interpretation device, as described in the following embodiment. Since the principle of solving the problems by these devices is similar to that of the log interpretation method, the implementation of these devices can be referred to as the implementation of the method, and the repetition is omitted.
Fig. 2 shows functional modules of a log interpretation apparatus according to an embodiment of the present invention, and for convenience of explanation, only parts relevant to the embodiment of the present invention are shown, which are described in detail below:
referring to fig. 2, each module included in the log interpretation apparatus is configured to perform each step in the corresponding embodiment of fig. 1, and refer specifically to fig. 1 and the related description in the corresponding embodiment of fig. 1, which are not repeated herein. In the embodiment of the present invention, the log interpretation device includes an acquisition module 201, a value range reconstruction module 202, a filtering module 203, a high-frequency velocity log acquisition module 204, a fluid characteristic curve determination module 205, and a pore fluid distribution determination module 206.
An acquisition module 201 is configured to acquire a longitudinal wave velocity log and a density log of a target reservoir.
The value range reconstruction module 202 is configured to reconstruct the value range of the longitudinal wave velocity log and the density log of the target reservoir, respectively, to obtain a longitudinal wave velocity log after the value range is reconstructed and a density log after the value range is reconstructed.
The filtering module 203 is configured to perform long wavelength filtering on the density log after the reconstruction range, and obtain compaction trend information of the target reservoir.
The high-frequency velocity log obtaining module 204 is configured to obtain a high-frequency velocity log of the target reservoir by subtracting compaction trend information of the target reservoir from the longitudinal velocity log after the range is reconstructed.
A fluid profile determination module 205 for determining a fluid profile of the target reservoir from the high frequency velocity log.
The pore fluid distribution determination module 206 is configured to determine a distribution of a target reservoir pore fluid based on a fluid profile of the target reservoir.
In the embodiment of the present invention, the acquisition module 201 acquires a longitudinal wave velocity log and a density log of the target reservoir, the value range reconstruction module 202 performs value range reconstruction on the longitudinal wave velocity log and the density log of the target reservoir, respectively, acquires a longitudinal wave velocity log after the value range is reconstructed and a density log after the value range is reconstructed, the filtering module 203 performs long wavelength filtering on the density log after the value range is reconstructed, acquires compaction trend information of the target reservoir, the high frequency velocity log acquisition module 204 subtracts the compaction trend information of the target reservoir from the longitudinal wave velocity log after the value range is reconstructed, acquires a high frequency velocity log of the target reservoir, the fluid characteristic curve determination module 205 determines a fluid characteristic curve of the target reservoir according to the high frequency velocity log, and the pore fluid distribution determination module 206 determines a distribution of pore fluid of the target reservoir according to the fluid characteristic curve of the target reservoir. In the embodiment of the present invention, in the case that the transverse wave log curve is missing, the high-frequency velocity log curve acquisition module 204 subtracts compaction trend information of the target reservoir from the longitudinal wave velocity log curve after the value range is reconstructed to obtain the high-frequency velocity log curve of the target reservoir, and the fluid characteristic curve determination module 205 further determines a fluid characteristic curve of the target reservoir according to the high-frequency velocity log curve, wherein the direct current component of the density log curve indicates compaction trend information of the target reservoir, the fluid characteristic curve indicates velocity dispersion characteristics of the target reservoir, and the fluid characteristic curve after the compaction trend information of the target reservoir is removed can improve accuracy and precision of predicting pore fluid distribution of the target reservoir.
In one embodiment of the present invention, the range of the longitudinal wave velocity log after reconstructing the range and the density log after reconstructing the range is between 0 and 1.
In an embodiment of the present invention, in order to further improve the accuracy and precision of predicting the pore fluid distribution of the target reservoir, each unit included in the value range reconstruction module 202 in the log interpretation apparatus is used to execute each step in the corresponding embodiment of the log interpretation method, and detailed descriptions of the corresponding embodiment of the log interpretation method will be omitted herein. In the embodiment of the present invention, the value range reconstruction module 202 in the log interpretation device includes a first determining unit and a first obtaining unit.
And the first determining unit is used for determining the maximum value and the minimum value of the longitudinal wave speed logging curve.
The first acquisition unit is used for reconstructing the value range of the longitudinal wave velocity log curve of the target reservoir according to the longitudinal wave velocity log curve, the maximum value and the minimum value of the longitudinal wave velocity log curve, and obtaining the longitudinal wave velocity log curve after reconstructing the value range.
In the embodiment of the invention, the first determining unit determines the maximum value and the minimum value of the longitudinal wave velocity logging curve, and the first obtaining unit carries out value range reconstruction on the longitudinal wave velocity logging curve of the target reservoir according to the longitudinal wave velocity logging curve and the maximum value and the minimum value thereof, so as to obtain the longitudinal wave velocity logging curve after the value range reconstruction, and further improve the accuracy and precision of predicting the pore fluid distribution of the target reservoir.
In an embodiment of the present invention, in order to further improve the accuracy and precision of predicting the pore fluid distribution of the target reservoir, each unit included in the value range reconstruction module 202 in the log interpretation apparatus is used to execute each step in the corresponding embodiment of the log interpretation method, and detailed descriptions of the corresponding embodiment of the log interpretation method will be omitted herein. In the embodiment of the present invention, the value range reconstruction module 202 in the log interpretation device includes a second determining unit and a second obtaining unit.
And the second determining unit is used for determining the maximum value and the minimum value of the density logging curve.
And the second acquisition unit is used for reconstructing the value range of the density logging curve of the target reservoir according to the density logging curve and the maximum value and the minimum value of the density logging curve to obtain the density logging curve after reconstructing the value range.
In the embodiment of the invention, the second determining unit determines the maximum value and the minimum value of the density logging curve, and the second obtaining unit carries out the value range reconstruction on the density logging curve of the target reservoir according to the density logging curve and the maximum value and the minimum value thereof, so as to obtain the density logging curve after the value range reconstruction, and further improve the accuracy and precision of predicting the pore fluid distribution of the target reservoir.
In an embodiment of the present invention, in order to further improve accuracy and precision of predicting the pore fluid distribution of the target reservoir, each unit included in the filtering module 203 in the log interpretation apparatus is used to perform each step in the corresponding embodiment of the log interpretation method, and specific reference is made to the description related to the corresponding embodiment of the log interpretation method, which is not repeated herein. In the embodiment of the present invention, the filtering module 203 in the log interpretation device includes a filtering operator determining unit and a filtering unit.
The filter operator determining unit is used for constructing a filter and determining a filter operator of the filter;
and the filtering unit is used for carrying out long-wavelength filtering on the density logging curve after the reconstruction value range by utilizing a filtering operator of the filter to obtain compaction trend information of the target reservoir.
In the embodiment of the invention, the filter operator determining unit constructs the filter, determines the filter operator of the filter, and carries out long-wavelength filtering on the density logging curve after the reconstruction value range by using the filter operator of the filter to obtain compaction trend information of the target reservoir, so that the accuracy and precision of predicting pore fluid distribution of the target reservoir can be further improved.
In an embodiment of the present invention, in order to further improve accuracy and precision of predicting the pore fluid distribution of the target reservoir, each unit included in the fluid characteristic curve determining module 205 in the log interpretation apparatus is used to execute each step in the corresponding embodiment of the log interpretation method, and detailed descriptions in the corresponding embodiment of the log interpretation method will be omitted herein. In the embodiment of the present invention, the fluid characteristic curve determining module 205 in the log interpretation device includes a laplace transform unit and a fluid characteristic curve determining unit.
The Laplace transformation unit is used for carrying out Laplace transformation on the high-frequency speed logging curves to obtain speed logging curves corresponding to different frequencies;
and the fluid characteristic curve determining unit is used for determining the fluid characteristic curve of the target reservoir according to the speed logging curves corresponding to different frequencies.
In the embodiment of the invention, the Laplace transformation unit performs Laplace transformation on the high-frequency velocity logging curves to obtain velocity logging curves corresponding to different frequencies, and the fluid characteristic curve determining unit determines the fluid characteristic curve of the target reservoir according to the velocity logging curves corresponding to different frequencies, so that the accuracy and precision of predicting the pore fluid distribution of the target reservoir can be further improved.
The implementation and functional principles of the present invention are described below in conjunction with an actual reservoir in a region:
firstly, acquiring a longitudinal wave velocity log curve and a density log curve of an actual reservoir, and further respectively reconstructing the value ranges of the longitudinal wave velocity log curve and the density log curve of the actual reservoir to obtain the longitudinal wave velocity log curve after reconstructing the value ranges and the density log curve after reconstructing the value ranges. In an embodiment of the invention, the actual reservoir depth range is a depth range of 2000 meters to 2300 meters.
Wherein fig. 3 is a longitudinal wave velocity curve after the actual reservoir reconstruction range, and fig. 4 is a density log after the actual reservoir reconstruction range. As can be seen from fig. 3 and 4, the range of the actual reservoir longitudinal wave velocity curve after the range is reconstructed and the range of the density log after the range is reconstructed are both between (0, 1).
And further, long wavelength filtering is carried out on the density logging curve after the actual reservoir is reconstructed in the value range in fig. 4, so as to obtain compaction trend information of the target reservoir, namely, the direct current component of the density logging curve after the value range is reconstructed.
Fig. 5 shows compaction trend information of the actual reservoir, i.e. the form of the dc component of the density log after the reconstruction of the value range, and it can be seen from fig. 5 that the high frequency details of the density log after the long wavelength filtering process are completely suppressed, and only the low frequency part reflecting the compaction trend information of the actual reservoir, i.e. the dc component of the density log after the reconstruction of the value range, is retained. As formation depositions continue and slow, the dc component of the density log changes smoothly and is not affected by differences in microscopic properties of the rock in the actual reservoir.
And then subtracting compaction trend information of the target reservoir by using the longitudinal wave velocity curve after the actual reservoir reconstruction value range in fig. 3, namely, the direct current component of the density logging curve after the reconstruction value range shown in fig. 5, so as to obtain the high-frequency velocity logging curve of the target reservoir shown in fig. 6.
As can be seen from fig. 6, the high-frequency velocity log curve does not reflect the direct current component of the actual reservoir pressure compaction trend information, and only contains the high-frequency velocity amplitude abnormal response caused by the physical differences of microscopic pores, pore fluids and the like, and the distribution rule of the pore fluids, the type of the pore fluids and the like of the actual reservoir can be deduced from the high-frequency velocity amplitude abnormal responses in fig. 6.
After the high-frequency velocity log shown in fig. 6 is obtained, the high-frequency velocity log is subjected to laplace transformation, so that a fluid characteristic curve of an actual reservoir is obtained.
Fig. 7 is a graph of the fluid characteristics obtained for an actual reservoir. The abscissa in fig. 7 is the magnitude of the fluid characteristic curve, and the ordinate is the depth. As can be seen from fig. 7, the fluid characteristic has an amplitude anomaly in the depth range of 2150 meters to 2250 meters, where the amplitude of the fluid characteristic is significantly greater than the upper and lower layers, indicating that the depth segment may be a hydrocarbon layer and the upper and/or lower layers may be a water layer or a dense layer.
Finally, when determining the distribution of the pore fluid of the target reservoir according to the fluid characteristic curve of the target reservoir, the target reservoir is interpreted by using the fluid characteristic curve, and the interpretation conclusion is that the oil and gas layer exists in the depth range of 2150 meters to 2250 meters, and the amplitude of the fluid characteristic curve is obviously larger than the amplitude in the depth range of 2200 meters to 2250 meters because of the depth range of 2150 meters to 2200 meters, according to the rock theory: the reservoir oil content can generate stronger absorption attenuation effect on the stratum relative to the reservoir oil content, so that the depth range of 2150 meters to 2200 meters is supposed to be the air layer, the depth range of 2150 meters to 2200 meters is supposed to be the oil layer, two perforation suggestions of 2223 meters (# 1) and 2188 meters (# 2) are submitted according to logging interpretation results, and the results show that 2223 meters (# 1) and 2188 meters (# 2) are respectively the oil layer and the air layer, and are completely consistent with the prediction conclusion. A shallow sample of oil 2050 meters is subsequently submitted (# 3), and the result shows a water layer. The three-time oil test result is completely consistent with the situation of the reservoir pore fluid distribution rule based on the prediction of the characteristic curve of the fluid, and the effectiveness of the method and the device for well logging interpretation provided by the embodiment of the invention is verified, so that the distribution rule of the reservoir pore fluid can be accurately predicted, the accuracy of reservoir prediction and pore fluid detection is improved, and the oil and gas exploration cost and risk are reduced.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the well logging interpretation method when executing the computer program.
Embodiments of the present invention also provide a computer-readable storage medium storing a computer program for executing the above-described log interpretation method.
In summary, the embodiment of the invention obtains the longitudinal wave velocity log curve and the density log curve of the target reservoir, respectively carries out value range reconstruction on the longitudinal wave velocity log curve and the density log curve of the target reservoir, obtains the longitudinal wave velocity log curve after the value range is reconstructed and the density log curve after the value range is reconstructed, carries out long wavelength filtering on the density log curve after the value range is reconstructed, obtains compaction trend information of the target reservoir, subtracts the compaction trend information of the target reservoir by utilizing the longitudinal wave velocity log curve after the value range is reconstructed, obtains the high-frequency velocity log curve of the target reservoir, determines the fluid characteristic curve of the target reservoir according to the high-frequency velocity log curve, and determines the distribution of the pore fluid of the target reservoir according to the fluid characteristic curve of the target reservoir. According to the embodiment of the invention, under the condition that the transverse wave logging curve is missing, the compaction trend information of the target reservoir is subtracted from the longitudinal wave velocity logging curve after the value range is reconstructed, the high-frequency velocity logging curve of the target reservoir is obtained, the fluid characteristic curve of the target reservoir is further determined according to the high-frequency velocity logging curve, the direct current component of the density logging curve shows the compaction trend information of the target reservoir, the fluid characteristic curve shows the velocity dispersion characteristic of the target reservoir, and the fluid characteristic curve after the compaction trend information of the target reservoir is removed can improve the accuracy and precision of predicting the pore fluid distribution of the target reservoir.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. A method of log interpretation comprising:
acquiring a longitudinal wave velocity log curve and a density log curve of a target reservoir;
reconstructing the value range of the longitudinal wave velocity log curve and the density log curve of the target reservoir respectively to obtain the longitudinal wave velocity log curve after reconstructing the value range and the density log curve after reconstructing the value range;
long wavelength filtering is carried out on the density logging curve after the reconstruction value range, and compaction trend information of the target reservoir is obtained;
subtracting compaction trend information of the target reservoir from the longitudinal wave velocity log after the value range is reconstructed to obtain a high-frequency velocity log of the target reservoir;
determining a fluid characteristic curve of the target reservoir according to the high-frequency speed logging curve;
determining the distribution of pore fluid of the target reservoir according to the fluid characteristic curve of the target reservoir;
determining a fluid profile of the target reservoir from the high frequency velocity log, comprising:
carrying out Laplace transformation on the high-frequency speed logging curve to obtain speed logging curves corresponding to different frequencies;
determining a fluid characteristic curve of the target reservoir according to the speed logging curves corresponding to different frequencies;
laplacian transformation is performed on the high frequency velocity log by the following formula:
Wherein V is 3 (j, f) represents velocity logs corresponding to different frequencies, x represents depth V at a location in the target reservoir 3 (x) Representing the value of the velocity profile corresponding to the depth x, lambda a Representing an adjustment factor of the Laplace transform, p representing an adjustment power coefficient of the Laplace transform, and f representing a frequency;
determining a fluid profile of the target reservoir by the following formula:
wherein A represents the fluid profile of the target reservoir, V 3 (j, f) represents different frequenciesRate-corresponding velocity log, V 3 (j,f 0 ) Representing a velocity log corresponding to a reference frequency, f representing frequency, f 0 Representing the reference frequency.
2. The method of claim 1, wherein long wavelength filtering the reconstructed value range density log to obtain compaction trend information for the target reservoir comprises:
constructing a filter and determining a filtering operator of the filter;
and long wavelength filtering is carried out on the density logging curve after the reconstruction value range by using a filtering operator of the filter, so as to obtain compaction trend information of the target reservoir.
3. A log interpretation apparatus, comprising:
the acquisition module is used for acquiring a longitudinal wave velocity log curve and a density log curve of the target reservoir;
The value range reconstruction module is used for reconstructing the value range of the longitudinal wave velocity log curve and the density log curve of the target reservoir respectively to obtain the longitudinal wave velocity log curve after the value range is reconstructed and the density log curve after the value range is reconstructed;
the filtering module is used for carrying out long-wavelength filtering on the density logging curve after the reconstruction value range to obtain compaction trend information of the target reservoir;
the high-frequency velocity logging curve acquisition module is used for subtracting compaction trend information of the target reservoir from the longitudinal wave velocity logging curve after the value range is reconstructed to obtain the high-frequency velocity logging curve of the target reservoir;
the fluid characteristic curve determining module is used for determining a fluid characteristic curve of the target reservoir according to the high-frequency speed logging curve;
the pore fluid distribution determining module is used for determining the distribution of pore fluid of the target reservoir according to the fluid characteristic curve of the target reservoir;
the fluid profile determination module includes:
the Laplace transformation unit is used for carrying out Laplace transformation on the high-frequency speed logging curves to obtain speed logging curves corresponding to different frequencies;
the fluid characteristic curve determining unit is used for determining a fluid characteristic curve of the target reservoir according to the speed logging curves corresponding to different frequencies;
Laplacian transformation is performed on the high frequency velocity log by the following formula:
wherein V is 3 (j, f) represents velocity logs corresponding to different frequencies, x represents depth V at a location in the target reservoir 3 (x) Representing the value of the velocity profile corresponding to the depth x, lambda a Representing an adjustment factor of the Laplace transform, p representing an adjustment power coefficient of the Laplace transform, and f representing a frequency;
determining a fluid profile of the target reservoir by the following formula:
wherein A represents the fluid profile of the target reservoir, V 3 (j, f) represents velocity log curves corresponding to different frequencies, V 3 (j,f 0 ) Representing a velocity log corresponding to a reference frequency, f representing frequency, f 0 Representing the reference frequency.
4. The apparatus of claim 3, wherein the filtering module comprises:
the filter operator determining unit is used for constructing a filter and determining a filter operator of the filter;
and the filtering unit is used for carrying out long-wavelength filtering on the density logging curve after the reconstruction value range by utilizing a filtering operator of the filter to obtain compaction trend information of the target reservoir.
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 2 when executing the computer program.
6. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the method of any one of claims 1 to 2.
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