A shale oil reservoir texture identification method based on wavelet decomposition

Abstract

The application discloses a shale oil reservoir texture identification method based on wavelet decomposition and relates to the technical field of oil and natural gas exploration. The application is based on full borehole covering electric imaging button conductivity array data, extracts the local abrupt point position of the original conductivity signal through one-dimensional wavelet change, then determines the maximum and minimum values by using the first derivative and the second derivative, and finally realizes the pickup of the sandy laminations and shale foliation of the shale oil reservoir by using the least square method to fit a sine function. The example result shows that the method has good texture pickup effect, the model can be developed into a software system, the required parameters are simple, and the method has good popularization advantages.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas exploration, more particularly to a shale oil reservoir texture identification method based on wavelet decomposition. BACKGROUND

[0002] The sandy laminations and shale foliation of lacustrine shale oil reservoirs effectively improve the reservoir fluid percolation properties and are beneficial to the formation and extension of fracturing cracks during fracturing, so the sandy laminations and shale foliation have a very important influence on subsequent production. However, the lacustrine shale oil reservoirs are affected by the source and sedimentary facies belt, and the lithological combination is complex and the sand-mud transition is fast in the vertical direction, resulting in difficulty in fine picking of the sandy laminations and shale foliation of the shale oil reservoirs.

[0003] The identification of the sandy laminations and shale foliation of the lacustrine shale oil reservoirs is mainly through quantitative characterization of core observation, and then qualitative evaluation by calibrating logging curves, but due to the lack of drilling core data and the low resolution of conventional logging curves, it is difficult to effectively identify. There are also quantitative evaluations of the sandy laminations and shale foliation based on electrical imaging images, but the calibration and processing of the images also affect the picking effect, and the picking technology of the sandy laminations and shale foliation of the shale oil reservoirs is difficult to be widely promoted due to the experience of interpreters, which brings great challenges to the exploration of the shale oil reservoirs.

[0004] Therefore, there is an urgent need for a quantitative picking method of the sandy laminations and shale foliation of the shale oil reservoir based on electrical imaging logging data to solve the above problems and guide the smooth implementation of the evaluation work of the continental shale gas reservoir. SUMMARY

[0005] In order to overcome the defects and deficiencies in the prior art, the present application provides a shale oil reservoir texture identification method based on wavelet decomposition, and the purpose of the present application is to solve the problems of poor picking effect and inaccurate picking of the shale oil reservoir texture in the prior art. The present application is based on the full borehole coverage of the electrical imaging button conductivity array data, the local abrupt point position of the original conductivity signal is extracted through one-dimensional wavelet change, then the maximum and minimum values are determined by using the first derivative and the second derivative, and finally the picking of the sandy laminations and shale foliation of the shale oil reservoir is realized by using the least square method to fit the sine function. The results of the examples show that the method proposed in the present application has good texture picking effect, and the model can be developed into a software system, the required parameters are simple, and it has good popularization advantages.

[0006] In order to solve the problems in the prior art, the present application is realized by the following technical scheme.

[0007] The present application provides a shale oil reservoir texture identification method based on wavelet decomposition, which comprises the following steps,

[0008] S1, preparing full bore coverage electrical imaging button electrode conductivity array data;

[0009] S2, based on the full bore coverage electrical imaging button electrode conductivity column data, for a given window length, performing vertical direction multi-order one-dimensional wavelet transform on the conductivity N column data;

[0010] S3, calculating the first derivative and the second derivative of the wavelet transformed qth order wavelet transform spectrum;

[0011] S4, determining the extreme value point of the conductivity curve based on the first derivative and the second derivative, at which the extreme value point indicates the local abrupt point position of the original signal;

[0012] S5, determining the maximum value and the minimum value point based on the first derivative and the second derivative, combining the petrophysical characteristics of the sandy lamina and the shale foliation, and dividing the maximum value point into the shale foliation and the minimum value point into the sandy lamina;

[0013] S6, establishing a coordinate system based on the conductivity data matrix, and extracting the extreme value point coordinates belonging to the sandy lamina and the shale foliation;

[0014] S7, selecting a suitable window, and performing least square sine function fitting on the extreme value point coordinates in the window to obtain a sine function expression;

[0015] S8, picking up the sandy lamina and the shale foliation through the sine function expression, thereby realizing the texture identification of the shale oil reservoir.

[0016] Further preferably, in the S1 step, the full bore coverage electrical imaging button electrode conductivity array data is composed of M rows and N column data, and the expression of the array data D is

[0017] ;

[0018] In the formula, is the conductivity of the imaging button point.

[0019] Further preferably, in the S2 step, when performing the vertical direction multi-order one-dimensional wavelet transform on the conductivity N column data, the morlet wavelet function is adopted.

[0020] Further preferably, when performing the one-dimensional wavelet transform on the conductivity of the Nth column in the array data D, the morlet wavelet function is adopted, and the scale function formula is as follows:

[0021] ​​​​

[0022] where x is the input conductivity value, S / m; is the center frequency of the scaling function.

[0023] Further preferably, the scaling function is scaled and shifted by an integer multiple to obtain a set of wavelet functions , which is expressed as:

[0024]

[0025] where q is the scaling factor, which scales the wavelet function by changing the size of q; k is the time shift factor, which shifts the wavelet function on the coordinate axis.

[0026] Further preferably, the expression of the k-th order wavelet transform of the M conductivities of the j-th column in the array data D is:

[0027]

[0028] where M is the number of conductivities, where:

[0029]

[0030]

[0031] where, [ , ,..., ]; Q represents the optional maximum value of the scaling function; represents the scaling function; k is the time shift factor; represents the wavelet function; represents the wavelet transform value calculated by selecting the wavelet function under the condition of different scaling factors and time shift factors; represents the calculation value of the scaling function when the scaling factor and the time shift factor are 0; x represents the input conductivity value.

[0032] Further preferably, in step S3, the first and second derivatives of the q-th order wavelet transform spectrum after wavelet transform are calculated, which is specifically:

[0033] The first and second derivatives of the k-th order wavelet transform of the j-th column conductivity are calculated to determine the extreme points, and the specific expression is as follows:

[0034]

[0035]

[0036] When​​ and at this time is the local mutation point position of the original signal; is the number of extreme points in a certain window range.

[0037] Further preferably, in the S5 step, the judgment of whether the extreme point belongs to sandy laminae or shale foliation adopts the method of minimum value and maximum value, specifically

[0038] Sandy laminae are high resistance compared to overlying and underlying lithology, which is expressed as a low value, i.e. a minimum value point, on the conductivity curve;

[0039] Shale foliation is low resistance compared to adjacent shale due to the development of foliation joints, which is expressed as a high value, i.e. a maximum value point, on the conductivity curve;

[0040] Based on the first derivative and the second derivative, the texture can be distinguished into sandy laminae and shale foliation, and the specific expression is as follows:

[0041] .

[0042] Further preferably, in the S6 step, a coordinate system is established based on the conductivity data matrix, specifically,

[0043] The conductivity data matrix is rewritten as a coordinate matrix, and the expression is:

[0044]

[0045] The corresponding coordinate points of the above solution are extracted , and the coordinate points in the depth range of 1-3m are selected to perform least square fitting of the sine function, and the solution target function is:

[0046]

[0047] In the formula, A is the amplitude of the sine function; is the frequency of the sine function; is the initial phase of the sine function; is the offset of the sine function.

[0048] More preferably, through the solution of the sine function expression, the related parameters of the texture are obtained, and the related parameters of the texture obtained by solving are used to pick up the sandy laminae and shale foliation of the shale oil reservoir.

[0049] More preferably, the related parameters of the texture include inclination parameters, dip angle parameters, angle parameters and length parameters.

[0050] Compared with the prior art, the beneficial technical effects brought by the present application are reflected in the following aspects:

[0051] The present application provides a shale oil reservoir texture identification method based on wavelet decomposition, which supplements the deficiency that conventional logging curves can only qualitatively analyze sandy laminae and shale lamella development segments, and at the same time, relative to the defect that the existing texture picking based on imaging diagrams does not consider the change information of the underlying conductivity curve, the present application proposes a shale oil reservoir texture identification method based on wavelet decomposition of the conductivity data matrix, which has good use effect, reduces the workload of manual texture identification, improves the texture identification efficiency, and has low resource requirement for calculation, and has good popularization advantage. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A flow chart of the shale oil reservoir texture identification method based on wavelet decomposition according to an embodiment of the present application is shown;

[0053] Figure 2 A one-dimensional morlet wavelet multi-order decomposition schematic diagram of a single column conductivity curve according to an embodiment of the present application is shown;

[0054] Figure 3 An imaging logging diagram sandy laminae and shale lamella identification effect schematic diagram according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be further clearly and completely explained and described in detail below in combination with the drawings in the embodiments of the present application. The embodiments described below are part of the embodiments of the present application, rather than all the embodiments of the present application; the illustrative and exemplary embodiments of the present application and their descriptions and drawings are only used to explain the present application, and do not limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0056] Embodiment 1

[0057] As a preferred embodiment of the present application, referring to the drawings in the specification, Figure 1 The present embodiment discloses a shale oil reservoir texture identification method based on wavelet decomposition, which comprises the following steps,

[0058] S1, preparing full borehole coverage electric imaging button electrode conductivity array data;

[0059] S2, based on the full borehole coverage electric imaging button electrode conductivity column data, for a given window length, performing multi-order one-dimensional wavelet transform in the vertical direction on the conductivity N column data;

[0060] S3, calculating first derivative and second derivative of the q-th wavelet transform spectrum after wavelet transform;

[0061] S4, determining extreme points of the conductivity curve based on the first derivative and the second derivative, where the extreme points indicate local abrupt point positions of the original signal;

[0062] S5, determining maximum value points and minimum value points based on the first derivative and the second derivative, and combining the petrophysical characteristics of the sandy laminae and the shale foliation to divide the maximum value points into the shale foliation and the minimum value points into the sandy laminae;

[0063] S6, establishing a coordinate system based on the conductivity data matrix and extracting the extreme point coordinates belonging to the sandy laminae and the shale foliation;

[0064] S7, selecting a suitable window, and performing least square sine function fitting on the extreme point coordinates in the window to obtain a sine function expression;

[0065] S8, picking up the sandy laminae and the shale foliation respectively through the sine function expression, so as to realize texture identification of the shale oil reservoir.

[0066] Embodiment 2

[0067] As another preferred embodiment of the present application, the embodiment is a further detailed supplement and elaboration of the technical solution of the present application based on the above-mentioned embodiment 1. In the embodiment, the full-bore covered electrical imaging button electrode conductivity array data is composed of M rows and N columns of data, and the expression of the array data D is

[0068]

[0069] In the formula, Dij is the conductivity of the i-th row and the j-th column imaging button point.

[0070] As an implementation manner of the embodiment, in the S2 step, when performing the vertical direction multi-order one-dimensional wavelet transform on the conductivity N column data, the morlet wavelet function is adopted. Specifically, when performing one-dimensional wavelet transform on the conductivity of the i-th row and the j-th column in the array data D, the morlet wavelet function is adopted, and the scale function formula is as follows:

[0071]

[0072] In the formula, x is the input conductivity value, S / m; and f0 is the center frequency of the scale function. ​​​​​​​

[0073] By scaling and shifting the scaling function by integer multiples, a set of wavelet functions is obtained. Its expression is:

[0074]

[0075] In the formula, q is the scaling factor, which scales the wavelet function by changing the size of q; k is the time shift factor, which translates the wavelet function on the coordinate axis.

[0076] For the M conductivity values ​​in the j-th column of the array data D The expression for the k-th order wavelet transform is:

[0077]

[0078] In the formula, M is the number of conductivity values. ,in:

[0079]

[0080]

[0081] In the formula, =[ , ,..., Q represents the optional maximum value of the scaling function; The scale function is represented by k, which is the time shift factor. Represents the wavelet function; This represents the wavelet transform values ​​calculated using a selected wavelet function under different scale factors and time shift factors. This represents the calculated value of the scaling function when the scaling factor and time shift factor are 0; x represents the input conductivity value.

[0082] Example 3

[0083] As another preferred embodiment of the present invention, this embodiment further supplements and elaborates on the technical solution of the present invention based on the above-described Embodiment 1 or Embodiment 2. In this embodiment, the first and second derivatives of the q-th order wavelet transform spectrum after wavelet transform are calculated, specifically,

[0084] The first The first and second derivatives of the k-th order wavelet transform of conductivity are used to determine the extreme points. The specific expression is as follows:

[0085]

[0086]

[0087] when and at this time is the local mutation point position of the original signal; is the number of extreme points in a certain window range.

[0088] Further, the judgment of whether the extreme point belongs to sandy lamina or shale foliation adopts the method of minimum value and maximum value, which is specifically

[0089] Sandy lamina is high resistance compared with overlying and underlying lithology, which is low value, that is, minimum point, on the conductivity curve;

[0090] Shale foliation is low resistance compared with adjacent shale due to the development of foliation seam, which is high value, that is, maximum point, on the conductivity curve;

[0091] Based on the first derivative and the second derivative, the texture can be distinguished into sandy lamina and shale foliation, and the specific expression is as follows:

[0092] .

[0093] In step S6, a coordinate system is established based on the conductivity data matrix, which is specifically

[0094] The conductivity data matrix is rewritten as a coordinate matrix, and the expression is:

[0095]

[0096] The above solving The corresponding coordinate points are extracted The coordinate points in the range of 1-3m are selected to perform least square fitting of the sine function, and the target function is:

[0097]

[0098] In the formula, A is the amplitude of the sine function; is the frequency of the sine function; is the initial phase of the sine function; is the offset of the sine function.

[0099] Further preferably, the related parameters of the texture are obtained by solving the expression of the sine function, and the related parameters of the texture obtained by solving are used to pick up the sandy lamina and shale foliation of the shale oil reservoir.

[0100] Further preferably, the related parameters of the texture include inclination parameters, dip angle parameters, angle parameters and length parameters.

[0101] Example 4

[0102] As another preferred embodiment of the present application, the embodiment takes a lacustrine shale oil reservoir XX well as an example to further illustrate the present application.

[0103] The embodiment provides a shale oil reservoir texture identification method based on wavelet decomposition. Figure 1 Figure 1 The wavelet decomposition-based shale oil reservoir texture identification method according to the embodiment of the present application is shown in the flowchart.

[0104] The imaging button conductivity array data of the whole well bore of the example is composed of 1000 rows and 360 columns of data, and the data example matrix is as follows:

[0105]

[0106] The data obtained by performing morlet one-dimensional 5th-order wavelet decomposition on the conductivity data in the first column is as follows:

[0107]

[0108] The first-order derivative and the second-order derivative of the 5th-order wavelet change of the conductivity in the first column are calculated, and the specific results are as follows:

[0109]

[0110] Based on the first-order derivative and the second-order derivative, the maximum and minimum points can be calculated, which are represented by different band codes in the coordinate system to form a three-element coordinate system, such as (1, 1, 1), the first two digits are coordinate values, and the last digit represents maximum or minimum, 0 represents no maximum or minimum at the coordinate point, -1 represents minimum, and 1 represents maximum, and the example results are as follows:

[0111]

[0112] The coordinate points with the third element being non-zero in the above three-element coordinate system are extracted, and the coordinate points in a certain window range are selected to perform least square fitting of a sine function, and the expression obtained by solving can obtain the sine function expression of the sandy laminations and shale lamellae, so that the sandy laminations and shale lamellae of the shale oil reservoir can be picked up.

[0113] It should be noted that in the present specification, the "example" description in the reference data contains part of the content in the present application technology, and is not limited to the same example.

[0114] ​​Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood that modifications can be made to the foregoing embodiments, or additional implementations of the present application can be implemented, without departing from the spirit or scope of the inventive subject matter. Accordingly, the present application is not limited to the implementations described herein, but is intended to be defined by the claims set forth below, and equivalents thereof.

Claims

1. A method for identifying the texture of a shale oil reservoir based on wavelet decomposition, characterized in that it comprises the steps of: The method comprises the following steps, ​ S1, preparing full borehole coverage electrical imaging button electrode conductivity array data; S2, based on the full borehole coverage electrical imaging button electrode conductivity column data, performing multi-order one-dimensional wavelet transform in the vertical direction on the conductivity N column data for a given window length; S3, calculating the first derivative and the second derivative of the qth-order wavelet transform spectrum after wavelet transform; S4, determining the extreme points of the conductivity curve based on the first derivative and the second derivative, wherein the extreme points indicate the local mutation point positions of the original signal; S5, determining the maximum value and the minimum value points based on the first derivative and the second derivative, and combining the petrophysical characteristics of the sandy laminations and the shale laminations to divide the maximum value points into the shale laminations and the minimum value points into the sandy laminations; S6, establishing a coordinate system based on the conductivity data matrix and extracting the extreme point coordinates belonging to the sandy laminations and the shale laminations; S7, selecting a suitable window, fitting the extreme point coordinates in the window into two categories by the least square sine function to obtain a sine function expression; S8, picking up the sandy laminations and the shale laminations respectively through the sine function expression, thereby realizing the texture identification of the shale oil reservoir.

2. The method of claim 1, wherein: In the S1 step, the full borehole coverage electrical imaging button electrode conductivity array data is composed of M rows and N columns of data, and the expression of the array data D is ； In the formula, is the first row of the column of the imaging button point product conductivity.

3. The method of claim 1, wherein: In the S2 step, the morlet wavelet function is used when performing multi-order one-dimensional wavelet transform in the vertical direction on the conductivity N column data.

4. The method of claim 3, wherein: The first column of the array data D Column of conductivity The morlet wavelet function is used in one-dimensional wavelet transform, and the scale function formula is as follows: where x is the input conductivity value, S / m; is the center frequency of the scale function.

5. The method of claim 4, wherein: Scaling and integer translation of the scale function results in a set of wavelet functions , the expression of which is In the formula, q is a scale factor, the wavelet function is stretched by changing the size of q; k is a time shift factor, and the wavelet function is translated on the coordinate axis.

6. The method according to any one of claims 1-5, wherein: In the S5 step, the minimum value and the maximum value method is used to determine whether the extreme points belong to the sandy laminations or the shale laminations, and specifically, The sandy laminations have high resistance compared with the overlying and underlying lithology, and are low in value and minimum value points on the conductivity curve; The shale laminations have low resistance compared with the adjacent shale due to the development of the shale laminations, and are high in value and maximum value points on the conductivity curve; Based on the first derivative and the second derivative, the texture is divided into the sandy laminations and the shale laminations.

7. The method according to any one of claims 1-5, wherein the method is based on wavelet decomposition. In the S6 step, the coordinate system is established based on the conductivity data matrix, and specifically, The conductivity data matrix is rewritten as a coordinate matrix, and the expression is The above solving The corresponding coordinate points are extracted The coordinate points in the range of 1-3 m are selected to perform least square fitting of the sinusoidal function, and the solving target function is: In the formula, A is the amplitude of the sine function; is the frequency of the sine function; is the initial phase of the sine function; is the offset of the sine function.

8. The method of claim 1-5, wherein the method is based on wavelet decomposition. Through solving the sine function expression, the related parameters of the texture are obtained, and the related parameters of the texture obtained by solving are used to pick up the sandy laminations and the shale laminations of the shale oil reservoir.

9. The method of claim 8, wherein: The related parameters of the texture include the inclination parameter, the dip angle parameter, the angle parameter and the length parameter.

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