CN116956119A - Sediment noise simulation-based shale layer sequence stratum division method and device - Google Patents
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Abstract
The invention discloses a shale layer sequence stratum partitioning method and device based on sediment noise simulation, comprising the following steps: (1) Selecting various logging curves for preprocessing, and then performing spectrum analysis to identify astronomical orbit periods; (2) Performing Gaussian band-pass filtering on the thickness corresponding to the astronomical orbit period in the logging curve, selecting a deep conversion model when the filtering curve is built, and obtaining time domain sequences of various logging curves; (3) Calculating time domain sequences of various logging curves through a sedimentary noise model to obtain different relative sea/lake plane change curves; (4) Carrying out principal component analysis on the median curves of different relative sea/lake level change curves, and extracting the maximum principal component PC1 curve; (5) And identifying the maximum value and the minimum value of the PC1 curve in the 1200 kiloyear or 2400 kiloyear period, and identifying the maximum sea/lake flooding surface and the layer sequence stratum interface. The invention can solve the problem of construction of the layer sequence stratigraphic framework in mudstone pages and the like in the prior art.
Description
Technical Field
The invention belongs to the technical field of shale stratum information processing, and particularly relates to a shale stratum sequence stratum partitioning method and device based on sedimentary noise simulation.
Background
The division and construction of conventional layer sequence strata is mainly based on the identification of 'deposition layer sequence' defined by the non-integration surface and the integration surface which can be corresponding to the non-integration surface. And for visual lithology changes such as shale are weak, and continuous layer sequence is deposited, the layer sequence interface inside the shale is difficult to identify. Shale segments are often treated as sea (lake) invasion system domains within a three-level sequence, where fine horizon calibration and analysis is difficult.
With the progress of unconventional shale oil and gas exploration and development, several methods for shale layer sequence stratigraphic classification have been raised in recent years: a causal layer sequence stratigraphic division method, a gamma energy spectrum gyratory analysis method, a chemical layer sequence stratigraphy method and the like.
The Chinese patent document with publication number of CN107817260A discloses a shale high-frequency sequence identification method, which comprises the following steps: acquiring geochemical information of core elements; selecting parameters capable of effectively reflecting lithology and ancient environment changes, and making a data curve; carrying out different scale moving average treatment on the data curve; and analyzing the relation between the data curve subjected to the moving average treatment and each layer sequence, and identifying the layer sequences with different scales by combining the gyrality of the data curve.
The Chinese patent document with publication number of CN107515957A discloses a shale layer sequence stratum division method, which comprises the following steps: extracting a core sample and obtaining element data and configuration characteristics; according to the characteristics of elements, analyzing the environmental evolution characteristics of paleoclimate, paleowater depth, paleosalinity and paleoredox; determining the interface characteristics of the quasi-layer sequence group according to the layer sequence gyratory standard by the layer sequence stratigraphy principle; establishing a corresponding relation between a natural gamma curve and a quasi-layer sequence group and layer sequence interfaces, and establishing a layer lattice profile; and carrying out full-region interval stratum division, drawing an interval stratum distribution plan, and analyzing an interval stratum distribution rule.
However, in the existing method, the interface and the layer sequence unit are mostly identified based on the change patterns (abrupt change, inflection point, ascending trend, descending trend change, etc.) of the data sequence, so that the influence of artificial subjective division factors on correctly identifying the layer sequence interface is large, and the division of the 'isochronous' layer unit grid is difficult.
Disclosure of Invention
The invention provides a shale layer sequence stratigraphic division method and device based on sediment noise simulation, which solve the problem of layer sequence stratigraphic framework construction in the prior art such as mudstone pages and the like.
A shale layer sequence stratum partitioning method based on sediment noise simulation comprises the following steps:
(1) Selecting various log curve data for preprocessing, so that the data are equally spaced and background white noise is removed; performing spectrum analysis on the preprocessed data to identify an astronomical orbit period;
(2) Performing Gaussian band-pass filtering on the thickness corresponding to the astronomical orbit period in the logging curve, selecting a time depth conversion model for building a filtering curve, and acquiring time domain sequences of various logging curves based on the time depth conversion model;
(3) Calculating time domain sequences of various logging curves through a sedimentary noise model to obtain different relative sea/lake plane change curves;
(4) Carrying out principal component analysis on the median curves of different relative sea/lake level change curves, and extracting the maximum principal component PC1 curve;
(5) And filtering the PC1 curve in the scale of 1200 kiloyears or 2400 kiloyears, and identifying the maximum sea/lake flooding surface and the layer sequence stratum interface at the maximum value and the minimum value of the filtering curve.
Preferably, in step (1), selecting various log data includes: natural gamma curve, resistivity curve, density curve, natural potential curve and porosity curve.
In the step (2), the identified astronomical orbit period is a long eccentricity period, the relation between the period and the corresponding thickness of the astronomical orbit period is required to meet the estimation of stratum deposition background and deposition rate, and the central frequency of participating in filtering calculation is the reciprocal of the thickness.
Preferably, in the step (2), the inverse of the thickness in the natural gamma data is used as the center frequency of the filtering, and when the time-depth conversion model is built, the time-depth conversion models of all logging curves are built in a unified manner according to the filtering curve obtained by the natural gamma.
Because of slight differences of stratum thicknesses corresponding to long eccentricity ratio periods stored in different logging sequences, the thickness periods in natural gamma data are usually referred to as central frequencies of filtering, and a time-depth conversion model of all logging sequences is uniformly established according to a filtering curve of the natural gamma logging sequences.
The specific process of the step (3) is as follows:
(3-1) after the time domain sequences of the various logs are imported into the Matlab program active software package, a window size of 300-500 kiloyears, or 400 kiloyears, is selected, which can be compared with a long eccentricity period. A small operating window would result in higher resolution, but the low frequency periodic variance and the total variance are reduced simultaneously, increasing the uncertainty of the non-astronomical signal ratio estimation.
(3-2) discriminating a relative change in water depth by detecting noise in the time domain sequence;
the noise is a non-orbit periodic signal in the time domain sequence and is obtained in the process of detecting an astronomical orbit period in the target time domain sequence; the target orbit parameters in the detection process are respectively a long eccentricity period, a short eccentricity period, a slope period and a time difference period, wherein the long eccentricity period corresponds to 405 kiloyears, the short eccentricity period corresponds to 125 or 95 kiloyears, the slope period corresponds to 40 kiloyears or less, and the time difference period corresponds to 23.6, 22.3 and 19.1 kiloyears or less;
(3-3) assuming that the noise distribution accords with normal distribution, comparing astronomical periodic signals and noise stored in a time domain sequence with astronomical orbit periods based on Monte Carlo simulation, analyzing the contribution of the astronomical period caused by the water depth related signals stored in the time domain sequence, taking the variance of the noise as an index of relative change of sea level, calculating the ratio of non-orbit signal variance and total variance in a sliding time window, carrying out iterative calculation for more than 1500 times to obtain a deposited noise model of each logging curve, and taking the median line of the model as a relative sea/lake plane change curve.
In step (3-2), the noise comprises age errors, sampling errors, differential compaction, unstable deposition and late diagenetic effects.
In the step (5), the 1200 kilo-year scale filtering adopts a center frequency of 8.33 multiplied by 10 -4 (reciprocal 1200 kiloyears), the 2400 kiloyear scale filtering employs a center frequency of 4.16X10 s corresponding to the ice period (new generation, late ao Tao Shi, late diad, etc.) of the geological history -4 (reciprocal 2400 kiloyears), corresponds to the greenhouse phase of geological history (chalky, early three folds, late cold Wu Shi, etc.).
The invention also provides a shale layer sequence stratigraphic division device based on sediment noise simulation, which comprises a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for realizing the shale layer sequence stratigraphic division method when executing the executable codes.
Compared with the prior art, the invention has the following beneficial effects:
1. based on Mi Lanke wiki theory, the method converts depth domain data into time domain data, and provides a basic unit with time significance for the isochronal division of the layer sequence stratum.
2. The invention is based on the sediment noise simulation, and draws a relative sea (lake) plane change curve through variance calculation of noise and total energy; because the noise stored in different logging data is different, the maximum principal component in each sedimentary noise curve can be identified according to principal component analysis, and the reflected water depth change is closer to the real water depth change. According to the change extremum of the filtering curve of the maximum principal component curve under the specific frequency, the layer sequence interface and the flooding surface inside the shale can be intuitively identified, so that the partition of the isochronous stratum grillwork is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic flow chart of a shale layer sequence stratigraphic division method based on sediment noise simulation;
FIG. 2 is a graph of spectral analysis of a natural gamma curve of a well after pretreatment according to an embodiment of the present invention;
FIG. 3 is a model of the deposited noise of various time domain log curves for a well according to an embodiment of the present invention;
FIG. 4 is a principal component analysis of a median curve of various types of sedimentary noise models for a well according to an embodiment of the present invention;
FIG. 5 shows the PC1 curve, PC1 filter curve and formation sequence division results for a well according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a shale layer sequence stratigraphic division method based on sedimentary noise simulation comprises the following steps:
s101, preprocessing various selected logging curves (natural gamma, resistivity, density, natural potential and porosity) to enable data to be equidistant and background white noise to be removed, importing open source software Matlab to conduct spectrum analysis, and identifying astronomical orbit periods.
Taking natural gamma well logging as an example, performing equidistant and background white noise removal treatment on the selected natural gamma well logging, introducing open source software for spectrum analysis, and identifying corresponding astronomical orbit period (as shown in figure 2, the test interval of frequency is 0-1.2 m) -1 The numbers in the figures are the reciprocal of the corresponding abscissa (frequency), i.e. thickness period. The ratio relation of each period of astronomical orbit parameters and each thickness can be deduced, wherein the thickness period of 21.27m corresponds to a long eccentricity period (405 kiloyears), and the estimated average deposition rate meets the judgment of regional geological background (deposition environment).
S102, performing Gaussian band-pass filtering on the identified depth corresponding to the astronomical orbit period, selecting a proper filtering curve according to the number of sine waves in the filtering curve and preliminary judgment on the stratum deposition background and the deposition rate, establishing a time-depth conversion model, and acquiring time domain sequences of various logging curves (natural gamma, resistivity, density, natural potential and porosity) based on the time-depth conversion model.
Subjecting the identified depth corresponding to astronomical orbit period, namely 21.27m, to Gaussian band-pass filtering, wherein the central frequency of the filtering is selected to be 1/21.27, namely 0.047m -1 . In fig. 2, the number of sine waves in the filtering curve is 15, the deposition thickness of a single well is 298m, and the deposition rate is identical to the preliminary determination in step S101, so that a deep conversion model can be built according to the filtering curve, i.e. one depth period corresponds to one 405 kiloyear. Based on the time-depth conversion model, a time domain sequence of each logging curve is obtained.
S103, calculating the time domain sequence through a deposited noise model to obtain several different relative sea level change curves.
Taking a time domain natural gamma sequence as an example, calculating the time domain natural gamma sequence through a deposited noise model to obtain a relative sea level change curve, wherein the specific process is as follows:
(1) the window size is chosen to be 300-500ka after the data is imported, comparable to the long eccentricity period.
(2) The relative change of the water depth is discriminated by detecting the astronomical orbit control signal, i.e. noise, in the sequence. The detection of noise is mainly based on the detection of the astronomical orbit period in the target time domain data. The target interval is early-stone-made, so that the selected target track parameters are as follows: long eccentricity (405 ka), short eccentricity (125 and 95 ka), slope (38 ka and 32 ka), time lapse (21.4, 20.4 and 18).
(3) Based on Monte Carlo simulation, comparing signals and noise stored in time domain data with astronomical periodic signals, analyzing contribution of signals related to water depth stored in a time domain sequence to astronomical period, calculating the ratio of non-orbit signal variance to total variance in a sliding time window by using the variance of the noise as an index of relative change of sea level, and performing iterative calculation for 2000 times to obtain a deposited noise model (shown in figure 3) of each logging curve, wherein a value line can indicate relative water depth change.
S104, carrying out principal component analysis on the relative water depth change curves of various well logging, and extracting the maximum principal component PC1.
The principal component analysis was performed on the median lines of the sedimentary noise models of the above log curves (natural gamma, resistivity, density, natural potential, porosity), and five median lines can extract five principal components, with the largest principal component PC1 accounting for 62.57% (as shown in fig. 4) selected as the curve that is most indicative of the relative water depth change of the borehole (as shown in fig. 5 a).
S105, identifying the maximum value and the minimum value of the PC1 curve in the 1200 or 2400 kiloyear period, and dividing the shale internal sequence interface and the flooding surface.
Since the selected objective interval is mainly in ice stage, the central frequency of PC1 is 8.33X10 -4 The obtained filtering curve (shown as b in fig. 5) is divided into a layer sequence interface and a maximum flooding surface according to the positions of the maximum value and the minimum value of the filtering curve, so as to construct a layer sequence layer lattice frame in the shale (shown as c in fig. 5).
The foregoing embodiments have described in detail the technical solution and the advantages of the present invention, it should be understood that the foregoing embodiments are merely illustrative of the present invention and are not intended to limit the invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the invention.
Claims (8)
1. The shale layer sequence stratum division method based on sediment noise simulation is characterized by comprising the following steps of:
(1) Selecting various log curve data for preprocessing, so that the data are equally spaced and background white noise is removed; performing spectrum analysis on the preprocessed data to identify an astronomical orbit period;
(2) Performing Gaussian band-pass filtering on the thickness corresponding to the astronomical orbit period in the logging curve, selecting a time depth conversion model for building a filtering curve, and acquiring time domain sequences of various logging curves based on the time depth conversion model;
(3) Calculating time domain sequences of various logging curves through a sedimentary noise model to obtain different relative sea/lake plane change curves;
(4) Carrying out principal component analysis on the median curves of different relative sea/lake level change curves, and extracting the maximum principal component PC1 curve;
(5) And filtering the PC1 curve in the scale of 1200 kiloyears or 2400 kiloyears, and identifying the maximum sea/lake flooding surface and the layer sequence stratum interface at the maximum value and the minimum value of the filtering curve.
2. The method of claim 1, wherein in step (1), selecting various log data comprises: natural gamma curve, resistivity curve, density curve, natural potential curve and porosity curve.
3. The method of claim 1, wherein in the step (2), the identified astronomical orbit period is a long eccentricity period, the relation of the thickness corresponding to the period should satisfy the estimation of the stratum deposition background and deposition rate, and the central frequency participating in the filtering calculation is the reciprocal of the thickness.
4. The method for classifying shale layer sequence strata based on sediment noise simulation according to claim 1, wherein in the step (2), the inverse of the thickness in natural gamma data is used as the central frequency of filtering, and when a time-depth conversion model is established, the time-depth conversion model of all logging curves is established uniformly according to a filtering curve obtained by natural gamma.
5. The method for stratifying a shale layer sequence based on sedimentary noise simulation of claim 1, wherein the specific process of the step (3) is as follows:
(3-1) importing time domain sequences of various logging curves into a Matlab program active software package, and selecting a window with a size of 300-500 kiloyears;
(3-2) discriminating a relative change in water depth by detecting noise in the time domain sequence;
the noise is a non-orbit periodic signal in the time domain sequence and is obtained in the process of detecting an astronomical orbit period in the target time domain sequence; the target orbit parameters in the detection process are respectively a long eccentricity period, a short eccentricity period, a slope period and a time difference period, wherein the long eccentricity period corresponds to 405 kiloyears, the short eccentricity period corresponds to 125 or 95 kiloyears, the slope period corresponds to 40 kiloyears or less, and the time difference period corresponds to 23.6, 22.3 and 19.1 kiloyears or less;
(3-3) assuming that the noise distribution accords with normal distribution, comparing astronomical periodic signals and noise stored in a time domain sequence with astronomical orbit periods based on Monte Carlo simulation, analyzing the contribution of the astronomical period caused by the water depth related signals stored in the time domain sequence, taking the variance of the noise as an index of relative change of sea level, calculating the ratio of the variance of non-orbit periodic signals to the total variance in a sliding time window, carrying out iterative calculation for more than 1500 times to obtain a deposited noise model of each logging curve, and taking the median line of the model as a relative sea/lake plane change curve.
6. The method of claim 5, wherein in step (3-2), the noise comprises age errors, sampling errors, differential compaction, unstable sediments, and late diagenetic effects.
7. The method for stratifying a shale layer sequence based on sedimentary noise simulation of claim 1, wherein in step (5), the 1200 kiloyear scale filtering uses a center frequency of 8.33 x 10 -4 The 2400 kiloyear scale filtering uses a center frequency of 4.16X10 corresponding to the ice period of the geological history -4 Greenhouse phase corresponding to geological history.
8. A shale layer sequence stratigraphic division apparatus based on depositional noise simulation, comprising a memory and one or more processors, the memory having executable code stored therein, the one or more processors, when executing the executable code, being configured to implement the shale layer sequence stratigraphic division method of any of claims 1-7.
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| CN117706646B (en) * | 2024-02-06 | 2024-04-19 | 山东万洋石油科技有限公司 | Logging instrument resistivity measurement optimization calibration method |
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