Shale oil reservoir texture identification method based on wavelet decomposition

Abstract

The invention discloses a shale oil reservoir texture identification method based on wavelet decomposition, and relates to the technical field of oil and gas exploration. The method comprises the following steps: extracting a local mutation point position of an original conductivity signal through one-dimensional wavelet change on the basis of electrical imaging button conductivity array data covered by a full borehole, and then determining a maximum value and a minimum value by using a first-order derivative and a second-order derivative; and finally, fitting a sine function by using a least square method to realize the pickup of the shale oil reservoir sandy texture layer and the shale lamella. Example results show that the method provided by the invention obtains a relatively good texture pickup effect, meanwhile, the model can be developed into a software system, required parameters are simple, and the method has a good popularization advantage.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration, and more specifically to a shale oil reservoir texture recognition method based on wavelet decomposition. Background Art

[0002] The sandy laminae and shale laminations of lacustrine shale oil reservoirs effectively improve the fluid seepage properties of the reservoir and are conducive to the formation and extension of fractures during the fracturing process. Therefore, the sandy laminae and shale laminations have a very important impact on the subsequent production. However, due to the influence of the provenance and sedimentary phase belts, the lacustrine shale oil reservoirs have complex lithological combinations and rapid sand-mud transitions in the vertical direction, which makes it difficult to finely pick up the sandy laminae and shale laminations of the shale oil reservoirs.

[0003] The identification of sandy laminae and shale laminae in lacustrine shale oil reservoirs is mainly through quantitative characterization of core observations, and then qualitative evaluation by calibrating well logging curves. However, due to the lack of drilling coring data and the low resolution of conventional well logging curves, it is difficult to effectively identify them. At present, there are also quantitative evaluations of sandy laminae and shale laminae based on electrical imaging images, but the calibration and processing of images are also related to the picking effect. Limited by the experience of interpreters, the current picking technology of sandy laminae and shale laminae in shale oil reservoirs is difficult to be widely promoted, which brings great challenges to the exploration of shale oil reservoirs.

[0004] Therefore, a quantitative picking method for sandy laminations and shale lamellae in shale oil reservoirs based on electrical imaging logging data is urgently needed to solve the above problems and guide the smooth implementation of continental shale gas reservoir evaluation. Summary of the invention

[0005] In order to overcome the defects and shortcomings in the above-mentioned prior art, the present invention provides a shale oil reservoir texture recognition method based on wavelet decomposition. The purpose of the present invention is to solve the problems of poor texture picking effect and inaccurate picking of shale oil reservoirs in the above-mentioned prior art. Based on the electrical imaging button conductivity array data with full borehole coverage, the present invention extracts the local mutation point position of the original conductivity signal through one-dimensional wavelet change, and then uses the first-order derivative and the second-order derivative to determine the maximum and minimum values. Finally, the least squares method is used to fit the sine function to realize the picking of sandy laminations and shale lamellae in shale oil reservoirs. The example results show that the method proposed in the present invention has a good texture picking effect. At the same time, the model can be developed into a software system with simple required parameters and good promotion advantages.

[0006] In order to solve the above problems existing in the prior art, the present invention is implemented through the following technical solutions.

[0007] The present invention provides a shale oil reservoir texture recognition method based on wavelet decomposition, the method comprising the following steps:

[0008] S1. Prepare electrical imaging button electrode conductivity array data covering the entire wellbore;

[0009] S2. Based on the conductivity column data of the electrical imaging button electrode covering the entire wellbore, for a given window length, a multi-order one-dimensional wavelet transform is performed on the N columns of conductivity data in the vertical direction;

[0010] S3, calculating the first-order derivative and the second-order derivative of the q-th order wavelet transform spectrum after wavelet transform;

[0011] S4, determining the extreme point of the conductivity curve based on the first-order derivative and the second-order derivative, where the extreme point indicates the position of the local mutation point of the original signal;

[0012] S5. Based on the first-order derivative and the second-order derivative, the maximum and minimum points are determined. Combined with the rock physical characteristics of the sandy laminae and shale lamina, the maximum points are classified as shale lamina and the minimum points are classified as sandy laminae.

[0013] S6. Establish a coordinate system based on the conductivity data matrix, and extract the coordinates of the extreme points belonging to the sandy laminae and shale lamina;

[0014] S7 selects a suitable window, divides the coordinates of the extreme value points in the window into two categories, performs least squares sine function fitting, and obtains a sine function expression;

[0015] S8. Sandy laminations and shale lamina can be picked up separately through the sine function expression, thereby realizing the texture recognition of shale oil reservoirs.

[0016] Further preferably, in step S1, the electrical imaging button electrode conductivity array data covering the entire borehole is composed of M rows and N columns of data, and the expression of the array data D is:

[0017]

[0018] In the formula, x ij The dot product conductivity of the imaging button in the i-th row and j-th column.

[0019] More preferably, in step S2, when performing multi-order one-dimensional wavelet transform in the vertical direction on the N columns of conductivity data, a morlet wavelet function is used.

[0020] More preferably, the conductivity X of the jth column in the array data D is j The Morlet wavelet function is used for one-dimensional wavelet transform, and its scaling function formula is as follows:

[0021]

[0022] Where x is the input conductivity value, S / m; w0 is the center frequency of the scaling function.

[0023] More preferably, the scale function is scaled and translated by integer multiples to obtain a wavelet function set Its expression is:

[0024]

[0025] In the formula, q is the scale factor, and the wavelet function is scaled by changing the size of q; k is the time shift factor, which realizes the translation of the wavelet function on the coordinate axis.

[0026] More preferably, for the M conductivity X in the jth column of the array data D j , the expression of the k-th order wavelet transform of j∈[1,N] is:

[0027]

[0028] Where M is the number of conductivity, Q = log2M, where:

[0029]

[0030]

[0031] In the formula, f(x)=[x 1j ,x 2j ,...,x Mj ]; Q represents the optional maximum value of the scaling function; φ(x) represents the scaling function; k is the time shift factor; ψ q,k (x) represents the wavelet function; T ψ (q, k) represents the wavelet transform value calculated by the selected wavelet function under different scale factors and time shift factors; T φ (0,0) represents the calculated value of the scaling function when the scaling factor and the time shift factor are 0; x represents the input conductivity value.

[0032] More preferably, in step S3, the first-order derivative and the second-order derivative of the q-th order wavelet transform spectrum after wavelet transform are calculated, specifically,

[0033] The k-th order wavelet change of the conductivity of the j-th column is used to obtain the first-order derivative and the second-order derivative to determine the extreme point. The specific expression is as follows:

[0034]

[0035]

[0036] When W′ k (x) = 0 and W″ kWhen (x)≠0, is the local mutation point position of the original signal; P is the number of extreme points within a certain window range.

[0037] Further preferably, in step S5, the method of minimum and maximum value is used to determine whether the extreme point belongs to the sandy laminae or the shale laminae, specifically:

[0038] The sandy laminae are highly resistive compared to the overlying and underlying lithologies, and appear as low values ​​on the conductivity curve, which is the minimum point;

[0039] Shale shales have low resistivity compared to adjacent shales due to the development of lamina fractures, which leads to mud invasion. They show high values ​​on the conductivity curve and are the maximum points.

[0040] Based on the first-order derivative and the second-order derivative, the extreme point type can be distinguished, and the texture can be divided into sandy lamination and shale lamination. The specific expression is as follows:

[0041]

[0042] Further preferably, in step S6, establishing a coordinate system based on the conductivity data matrix specifically refers to:

[0043] Rewrite the conductivity data matrix into a coordinate matrix, and its expression is:

[0044]

[0045] Solve the above for x * The corresponding coordinate points are extracted The coordinate points within the depth range of 1-3m are selected to perform the least squares fitting of the sine function, and the objective function to be solved is:

[0046]

[0047] Where A is the period of the sine function; ω is the frequency of the sine function; is the initial phase of the sine function; b is the offset of the sine function.

[0048] More preferably, by solving the sine function expression, texture related parameters are obtained, and the sandy laminations and shale lamellae of the shale oil reservoir are picked up by solving the obtained texture related parameters.

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

[0050] Compared with the prior art, the beneficial technical effects brought by the present invention are as follows:

[0051] The present invention provides a shale oil reservoir texture recognition method based on wavelet decomposition, which makes up for the deficiency that conventional logging curves can only qualitatively analyze sandy laminae and shale lamellae development sections. At the same time, compared with the defect that the underlying conductivity curve change information is not considered when picking up texture based on imaging images, a shale oil reservoir texture recognition method based on conductivity data matrix wavelet decomposition is proposed, which achieves better use effect, reduces the workload of manual texture recognition, improves texture recognition efficiency, and requires lower computing resources, thus having good promotion advantages. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0054] Figure 3 A schematic diagram showing the recognition effect of sandy laminations and shale lamina in an imaging logging image according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be further clearly and completely described and elaborated in detail in combination with the drawings in the embodiments of the present invention. The embodiments described below are part of the embodiments of the present invention, not all of the embodiments; the schematic and exemplary implementation methods of the present invention and their descriptions and schematic diagrams are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] Example 1

[0057] As a preferred embodiment of the present invention, refer to the attached specification Figure 1 As shown, this embodiment discloses a shale oil reservoir texture recognition method based on wavelet decomposition, the method comprising the following steps:

[0058] S1. Prepare electrical imaging button electrode conductivity array data covering the entire wellbore;

[0059] S2. Based on the conductivity column data of the electrical imaging button electrode covering the entire wellbore, for a given window length, a multi-order one-dimensional wavelet transform is performed on the N columns of conductivity data in the vertical direction;

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

[0061] S4, determining the extreme point of the conductivity curve based on the first-order derivative and the second-order derivative, where the extreme point indicates the position of the local mutation point of the original signal;

[0062] S5. Based on the first-order derivative and the second-order derivative, the maximum and minimum points are determined. Combined with the rock physical characteristics of the sandy laminae and shale lamina, the maximum points are classified as shale lamina and the minimum points are classified as sandy laminae.

[0063] S6. Establish a coordinate system based on the conductivity data matrix, and extract the coordinates of the extreme points belonging to the sandy laminae and shale lamina;

[0064] S7 selects a suitable window, divides the coordinates of the extreme value points in the window into two categories, performs least squares sine function fitting, and obtains a sine function expression;

[0065] S8. Sandy laminations and shale lamina can be picked up separately through the sine function expression, thereby realizing the texture recognition of shale oil reservoirs.

[0066] Example 2

[0067] As another preferred embodiment of the present invention, this embodiment is based on the above-mentioned embodiment 1, and further supplements and explains the technical solution of the present invention in detail. In this embodiment, the electrical imaging button electrode conductivity array data of the full borehole coverage is composed of M rows and N columns of data, and the expression of the array data D is:

[0068]

[0069] In the formula, x ij The dot product conductivity of the imaging button in the i-th row and j-th column.

[0070] As an implementation of this embodiment, in step S2, when performing a multi-order one-dimensional wavelet transform in the vertical direction on the N columns of conductivity data, a morlet wavelet function is used. Specifically, the conductivity X of the jth column in the array data D is j The Morlet wavelet function is used for one-dimensional wavelet transform, and its scaling function formula is as follows:

[0071]

[0072] Where x is the input conductivity value, S / m; w0 is the center frequency of the scaling function.

[0073] The scale function is scaled and translated by integer multiples to obtain a wavelet function set Its expression is:

[0074]

[0075] In the formula, q is the scale factor, and the wavelet function is scaled by changing the size of q; k is the time shift factor, which realizes the translation of the wavelet function on the coordinate axis.

[0076] For the M conductivity X in the jth column of the array data D j , the expression of the k-th order wavelet transform of j∈[1,N] is:

[0077]

[0078] Where M is the number of conductivity, Q = log2M, where:

[0079]

[0080]

[0081] In the formula, f(x)=[x 1j ,x 2j ,...,x Mj ]; Q represents the optional maximum value of the scaling function; φ(x) represents the scaling function; k is the time shift factor; ψ q,k (x) represents the wavelet function; T ψ (q, k) represents the wavelet transform value calculated by the selected wavelet function under different scale factors and time shift factors; T φ (0,0) represents the calculated value of the scaling function when the scaling factor and the 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 is a further detailed supplement and elaboration of the technical solution of the present invention based on the above-mentioned embodiment 1 or embodiment 2. In this embodiment, the first-order derivative and the second-order derivative of the q-th order wavelet transform spectrum after wavelet transform are calculated, specifically,

[0084] The k-th order wavelet change of the conductivity of the j-th column is used to obtain the first-order derivative and the second-order derivative to determine the extreme point. The specific expression is as follows:

[0085]

[0086]

[0087] When W′ k (x) = 0 and W″ k When (x)≠0, is the local mutation point position of the original signal; P is the number of extreme points within a certain window range.

[0088] Furthermore, the method of minimum and maximum values ​​is used to determine whether the extreme value points belong to sandy laminae or shale laminae.

[0089] The sandy laminae are highly resistive compared to the overlying and underlying lithologies, and appear as low values ​​on the conductivity curve, which is the minimum point;

[0090] Shale shales have low resistivity compared to adjacent shales due to the development of lamina fractures, which leads to mud invasion. They show high values ​​on the conductivity curve and are the maximum points.

[0091] Based on the first-order derivative and the second-order derivative, the extreme point type can be distinguished, and the texture can be divided into sandy lamination and shale lamination. The specific expression is as follows:

[0092]

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

[0094] Rewrite the conductivity data matrix into a coordinate matrix, and its expression is:

[0095]

[0096] Solve the above for x * The corresponding coordinate points are extracted Select coordinate points within the range of 1-3m to perform least squares fitting of the sine function, and the objective function to be solved is:

[0097]

[0098] Where A is the period of the sine function; ω is the frequency of the sine function; is the initial phase of the sine function; b is the offset of the sine function.

[0099] More preferably, by solving the sine function expression, texture related parameters are obtained, and the sandy laminations and shale lamellae of the shale oil reservoir are picked up by solving the obtained texture related parameters.

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

[0101] Example 4

[0102] As another preferred embodiment of the present invention, this embodiment takes a lacustrine shale oil reservoir XX well as an example to further elaborate the present invention in detail.

[0103] This embodiment provides a shale oil reservoir texture recognition method based on wavelet decomposition, which specifically includes the following steps: Figure 1As shown, Figure 1 A flow chart of a shale oil reservoir texture recognition method based on wavelet decomposition according to an embodiment of the present invention is shown.

[0104] The imaging button conductivity array data with full borehole coverage in the example is composed of 1000 rows and 360 columns of data, and the data example matrix D is shown as follows:

[0105]

[0106] The conductivity data of the first column is decomposed by Morlet one-dimensional 5th order wavelet as follows:

[0107]

[0108] The first-order derivative and second-order derivative of the fifth-order wavelet change of the first column conductivity are obtained, 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 and represented by different codes in the coordinate system to form a ternary coordinate system, such as (1, 1, 1). The first two digits are the coordinate values, and the second digit represents the maximum and minimum values. 0 means there is no maximum and minimum value at this coordinate point, -1 represents the minimum value, and 1 represents the maximum value. The example results are as follows:

[0111]

[0112] The coordinate points whose third element in the above three-dimensional coordinate system is not zero are extracted, and then the coordinate points within a certain window range are selected to perform least squares fitting of the sine function. The solved expression can obtain the sine function expression of the sandy lamination and shale lamina, thereby realizing the picking of sandy lamination and shale lamina in shale oil reservoirs.

[0113] It should be noted that, in this specification, the “example” description in the reference data includes part of the content of the technology of the present invention and is not limited to the same example.

[0114] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein, and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A shale oil reservoir texture recognition method based on wavelet decomposition, characterized in that: The method comprises the following steps, S1. Prepare electrical imaging button electrode conductivity array data covering the entire wellbore; S2. Based on the conductivity column data of the electrical imaging button electrode covering the entire wellbore, for a given window length, a multi-order one-dimensional wavelet transform is performed on the N columns of conductivity data in the vertical direction; S3, calculating the first-order derivative and the second-order derivative of the q-th order wavelet transform spectrum after wavelet transform; S4, determining the extreme point of the conductivity curve based on the first-order derivative and the second-order derivative, where the extreme point indicates the position of the local mutation point of the original signal; S5. Based on the first-order derivative and the second-order derivative, the maximum and minimum points are determined. Combined with the rock physical characteristics of the sandy laminae and shale lamina, the maximum points are classified as shale lamina and the minimum points are classified as sandy laminae. S6. Establish a coordinate system based on the conductivity data matrix, and extract the coordinates of the extreme points belonging to the sandy laminae and shale lamina; S7 selects a suitable window, divides the coordinates of the extreme value points in the window into two categories, performs least squares sine function fitting, and obtains a sine function expression; S8. Sandy laminations and shale lamina can be picked up separately through the sine function expression, thereby realizing the texture recognition of shale oil reservoirs.

2. The shale oil reservoir texture recognition method based on wavelet decomposition according to claim 1, characterized in that: In step S1, the electrical imaging button electrode conductivity array data of the entire borehole is composed of M rows and N columns of data. The expression of the array data D is: In the formula, x ij The dot product conductivity of the imaging button in the i-th row and j-th column.

3. The shale oil reservoir texture recognition method based on wavelet decomposition according to claim 1, characterized in that: In step S2, when performing multi-order one-dimensional wavelet transform in the vertical direction on N columns of conductivity data, the Morlet wavelet function is used.

4. The shale oil reservoir texture recognition method based on wavelet decomposition according to claim 3 is characterized in that: The conductivity X of the jth column in the array data D j The Morlet wavelet function is used for one-dimensional wavelet transform, and its scaling function formula is as follows: Where x is the input conductivity value, S / m; w0 is the center frequency of the scaling function.

5. The shale oil reservoir texture recognition method based on wavelet decomposition according to claim 4, characterized in that: The scale function is scaled and translated by integer multiples to obtain a wavelet function set Its expression is: In the formula, q is the scale factor, and the wavelet function is scaled by changing the size of q; k is the time shift factor, which realizes the translation of the wavelet function on the coordinate axis.

6. The shale oil reservoir texture recognition method based on wavelet decomposition according to claim 5, characterized in that: For the M conductivity X in the jth column of the array data D j , the expression of the k-th order wavelet transform of j∈[1,N] is: Where M is the number of conductivity, Q = log2M, where: In the formula, f(x)=[x 1j ,x 2j ,...,x Mj ]; Q represents the optional maximum value of the scaling function; φ(x) represents the scaling function; k is the time shift factor; ψ q,k (x) represents the wavelet function; T ψ (q, k) represents the wavelet transform value calculated by the selected wavelet function under different scale factors and time shift factors; T φ (0,0) represents the calculated value of the scaling function when the scaling factor and the time shift factor are 0; x represents the input conductivity value.

7. The shale oil reservoir texture recognition method based on wavelet decomposition according to claim 6, characterized in that: In step S3, the first-order derivative and the second-order derivative of the q-th order wavelet transform spectrum after wavelet transform are calculated, specifically, The k-th order wavelet change of the conductivity of the j-th column is used to obtain the first-order derivative and the second-order derivative to determine the extreme point. The specific expression is as follows: When W′ k (x) = 0 and W″ k When (x)≠0, is the local mutation point position of the original signal; P is the number of extreme points within a certain window range.

8. A shale oil reservoir texture recognition method based on wavelet decomposition according to any one of claims 1 to 7, characterized in that: In step S5, the method of minimum and maximum values ​​is used to determine whether the extreme value points belong to sandy laminations or shale laminations. The sandy laminae are highly resistive compared to the overlying and underlying lithologies, and appear as low values ​​on the conductivity curve, which is the minimum point; Shale shales have low resistivity compared to adjacent shales due to the development of lamina fractures, which leads to mud invasion. They show high values ​​on the conductivity curve and are the maximum points. Based on the first-order derivative and the second-order derivative, the extreme point type can be distinguished, and the texture can be divided into sandy lamination and shale lamination. The specific expression is as follows:

9. A shale oil reservoir texture recognition method based on wavelet decomposition according to any one of claims 1 to 7, characterized in that: In step S6, a coordinate system is established based on the conductivity data matrix, specifically, Rewrite the conductivity data matrix into a coordinate matrix, and its expression is: Solve the above for x * The corresponding coordinate points are extracted Select coordinate points within the range of 1-3m to perform least squares fitting of the sine function, and the objective function to be solved is: Where A is the period of the sine function; ω is the frequency of the sine function; is the initial phase of the sine function; b is the offset of the sine function.

10. A shale oil reservoir texture recognition method based on wavelet decomposition according to any one of claims 1 to 7, characterized in that: By solving the sine function expression, texture related parameters are obtained, and the sandy laminae and shale lamellae of the shale oil reservoir are picked up by solving the texture related parameters.

11. The shale oil reservoir texture recognition method based on wavelet decomposition according to claim 10, characterized in that: The texture related parameters include inclination parameter, dip parameter, angle parameter and length parameter.

Images