Natural potential curve mudstone baseline construction method
By constructing the mudstone baseline of the natural potential curve using a one-dimensional wavelet continuous transform algorithm, the problems of low interpretation accuracy and low efficiency in existing technologies are solved, and efficient and accurate automatic construction of mudstone baselines is achieved, which is suitable for mass production wells.
Patent Information
- Application Number
- CN202411653158.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2026-05-19
AI Technical Summary
Existing methods for constructing spontaneous potential mudstone baselines have low interpretation accuracy and efficiency, making them unsuitable for interpreting large numbers of production wells, and they also have large errors when the water flooding is severe.
A one-dimensional wavelet continuous transform algorithm is adopted to perform one-dimensional continuous wavelet transform on the spontaneous potential logging curve using the first derivative of Gauss as the basis wavelet, identify the waveform morphology, and construct the mudstone baseline of the spontaneous potential curve based on the identification results.
It enables the automatic construction of spontaneous potential mudstone baselines, improving interpretation accuracy and efficiency. It is applicable to high-volume production wells, simulating manual methods while improving work efficiency.
Smart Images
Figure CN122063686A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of well logging evaluation technology, and in particular to a method for constructing a mudstone baseline for spontaneous potential curves. Background Technology
[0002] Traditional methods for constructing spontaneous potential (SP) mudstone baselines fall into two categories. One is manual construction, where a mudstone baseline curve is manually drawn on a paper or electronic map based on the shape of the SP curve. When used, the measured SP curve value and the value of the connected mudstone baseline curve are read; the difference between the two values is the abnormal amplitude of the SP curve. Manually connecting mudstone baselines better accounts for phenomena such as SP curve shifts after high water flooding, but its disadvantage is low work efficiency and unsuitability for processing and interpreting large-scale production wells. The other method involves reading the SP measured value of the mudstone layer within the entire well section or a specific layer as the mudstone value for that layer. When used, the difference between the SP measured value and the mudstone value is the abnormal amplitude of the SP curve. While this method improves work efficiency, its accuracy is low, and it cannot account for phenomena such as SP curve shifts. In areas with severe water flooding and mudstone baseline shifts, the connected mudstone baseline is too large, resulting in an excessively large difference in the calculated abnormal amplitude of the SP curve, leading to significant errors. Summary of the Invention
[0003] This invention addresses the problems of low interpretation accuracy and low efficiency in existing spontaneous potential (SP) mudstone baseline construction methods, by providing a new method for constructing SP curve mudstone baselines. This method achieves automatic SP curve mudstone baseline construction, can continuously process multiple wells, has greater adaptability, and offers higher interpretation accuracy. It can be applied to the interpretation of infill wells, laying the foundation for the rational application of SP curves and formation interpretation.
[0004] The present invention solves its problem through the following technical solution: the method for constructing the mudstone baseline of the spontaneous potential curve includes the following steps:
[0005] S1: First, the algorithm of one-dimensional wavelet continuous transform is adopted, and the natural potential logging curve is transformed by one-dimensional continuous wavelet transform using the first derivative of Gauss as the basis wavelet.
[0006] S2: Design of a one-dimensional continuous wavelet transform algorithm;
[0007] S3: Based on the design of a one-dimensional continuous wavelet transform algorithm, waveform morphology is identified;
[0008] S4: Based on the identified waveform morphology, connect the mudstone baseline to construct the natural potential curve mudstone baseline.
[0009] Preferably, the method for obtaining the Gaussian first derivative of the spontaneous potential logging curve in step S1 is as follows:
[0010] 2.1 First, define the Gaussian function G(x), which has the following form:
[0011]
[0012] Where: σ is the standard deviation of the Gaussian function, which determines the width of the function, m;
[0013] x is the independent variable, representing a certain depth point on the spontaneous potential curve, m;
[0014] 2.2. Taking the first derivative of the Gaussian function G(x) yields the Gaussian first derivative function.
[0015]
[0016] Preferably, the specific method for designing the one-dimensional continuous wavelet transform algorithm in step S2 includes the following steps:
[0017] The specific method for designing the one-dimensional continuous wavelet transform algorithm in step S2 includes the following steps:
[0018] 3.1. Treat the natural potential curve SP as the signal SP(x);
[0019] 3.2. To perform wavelet transform, the Gaussian first derivative from step 2.2 is used for scaling and translation. The resulting function of the natural potential logging curve after scaling and translation is denoted as:
[0020]
[0021] Where: a is the scale factor, m;
[0022] b is the time shift factor, reflecting displacement, m;
[0023] x represents a depth point on the spontaneous potential curve, in meters (m).
[0024] The function of spontaneous potential logging curve after scaling and translation;
[0025] 3.3 Calculate the convolution of the spontaneous potential curve SP(x) with the function after scale-scaling translation to obtain the wavelet coefficients W of the spontaneous potential logging curve. sp (a,b):
[0026]
[0027] Substituting formula (3) into formula (4), the wavelet coefficient W spThe expression (a, b) is transformed into the following form:
[0028]
[0029] The above steps are the specific calculation process of the wavelet coefficients of the natural potential curve SP(x) at scale a and location b.
[0030] Preferably, in step S2, when the wavelet function can be regarded as the first derivative of a certain smooth function, the abrupt change point or edge of the wavelet transform mode of the signal corresponds to the local extreme point of the signal, and the obtained abrupt change point guides the identification of the waveform shape.
[0031] Preferably, the waveform in step S3 is divided into a positive waveform and a negative waveform, and each waveform has 3 characteristic values;
[0032] The three characteristic values of a positive waveform are: two minimum values and one maximum value;
[0033] The three characteristic values of a negative waveform are: two maxima and one minima.
[0034] Preferably, the positive waveform is defined as: a curve segment with exactly 3 extreme points, which appears in sequence as minimum point, maximum point, and minimum point.
[0035] Preferably, the negative waveform is defined as a curve segment with exactly three extreme points, which appears in sequence as a maximum point, a minimum point, and another maximum point.
[0036] Preferably, the method for identifying waveform morphology includes:
[0037] In practical applications, the point of abrupt change is used as a reference for determining the waveform limit.
[0038] Using the wavelet coefficients W of the spontaneous potential logging curve in step S3 sp (a,b) expression formula (5), traverse the entire natural potential curve from top to bottom according to the depth, record all the abrupt change points of the entire curve, and use all the abrupt change points of the entire natural potential curve to identify the waveform shape;
[0039] Find the maximum point in each positive and negative waveform, and record the magnitude and depth of the natural potential curve corresponding to each maximum point.
[0040] Preferably, the method for constructing the mudstone baseline of the spontaneous potential curve is as follows:
[0041] The markings are based on the maximum points and depth locations corresponding to each waveform of the natural potential curve that have been found.
[0042] Then, a depth window is set. Within each depth window, the maximum values of all natural potential curves within the window are compared, and the maximum value among all waveform maxima is found, its position and the corresponding natural potential amplitude are marked.
[0043] The depth window is then moved down by a fixed step and the above steps are repeated to ensure that the window covers the spontaneous potential curve within the entire depth segment. Finally, all the found maximum spontaneous potential values are connected, and the data between each maximum value is filled in using linear interpolation to form a continuous curve, i.e., the spontaneous potential mudstone baseline.
[0044] Preferably, the length of the depth window is set to 10-30m.
[0045] Compared with the above-mentioned background technology, the present invention has the following beneficial effects:
[0046] Compared with other methods for determining the mudstone baseline of spontaneous potential curves, the significant advantage of this invention lies in its ability to simulate the manual method of connecting spontaneous potential mudstone baselines, while being more efficient and applicable to the processing and interpretation of a large number of production wells. The intelligent technology for connecting spontaneous potential mudstone baselines greatly improves the utilization of spontaneous potential curves and represents a new trend in intelligent well logging evaluation. Attached Figure Description
[0047] The above and other objects, features and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:
[0048] Figure 1 This is a flowchart of the method for constructing the mudstone baseline of the spontaneous potential curve according to the present invention;
[0049] Figure 2 This is a diagram showing the interpretation results of the spontaneous potential mudstone baseline of well Zhong 44-Jian P204 in Embodiment 1 of the present invention;
[0050] Figure 3 This is a diagram showing the interpretation results of the spontaneous potential mudstone baseline of well Bei 1-Ding 5-Jian E48 in Embodiment 2 of the present invention. Detailed Implementation
[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
[0052] The present invention will now be described based on embodiments, but it is worth noting that the present invention is not limited to these embodiments. In the following detailed description of the invention, certain specific details are described in detail. However, those skilled in the art will fully understand the invention for the parts not described in detail.
[0053] Furthermore, those skilled in the art should understand that the accompanying drawings are provided only to illustrate the purpose, features, and advantages of the present invention, and are not actually drawn to scale.
[0054] Furthermore, unless the context explicitly requires it, the words "comprising," "including," and similar terms throughout the specification and claims should be interpreted as including rather than exclusive or exhaustive; that is, meaning "including but not limited to."
[0055] like Figure 1 As shown, a method for constructing a spontaneous potential curve mudstone baseline includes the following steps:
[0056] Step 1: First, a one-dimensional wavelet continuous transform algorithm is used, with the first derivative of Gaussian wavelets as the basis wavelet, to perform a one-dimensional continuous wavelet transform on the spontaneous potential logging curve. The advantage of using the first derivative of Gaussian wavelets as the basis wavelet is that it can highlight abrupt changes in the signal, which is crucial for identifying the extreme points of the spontaneous potential curve.
[0057] The method for obtaining the first derivative of Gaussian energy in the spontaneous potential curve is as follows:
[0058] First, define the Gaussian function G(x), which has the following form:
[0059]
[0060] Where: σ is the standard deviation of the Gaussian function, which determines the width of the function, m;
[0061] x is the independent variable, representing a certain depth point on the spontaneous potential curve, m.
[0062] Taking the first derivative of the Gaussian function G(x) yields the Gaussian first derivative function.
[0063]
[0064] Step 2: Design the one-dimensional continuous wavelet transform algorithm;
[0065] The spontaneous potential curve SP can be considered as a signal SP(x). To perform wavelet transform, we need to use the first-order Gaussian derivative from the first step to scale and translate it, obtaining the scaled and translated function of the spontaneous potential logging curve, denoted as:
[0066]
[0067] Where: a is the scale factor, m;
[0068] b is the time shift factor, reflecting displacement, m;
[0069] x represents a depth point on the spontaneous potential curve, in meters (m).
[0070] Then, the convolution of the natural potential curve SP(x) with the scale-scaled and translated function is calculated to obtain the wavelet coefficients W. sp (a,b):
[0071]
[0072] Substituting formula (3) into formula (4), the wavelet coefficient W sp The expression (a, b) is transformed into the following form:
[0073]
[0074] The above describes the specific calculation process of the wavelet coefficients of the natural potential curve SP(x) at scale a and location b.
[0075] Therefore, we can see that when the wavelet function can be regarded as the first derivative of a certain smooth function, the abrupt change point (or edge) of the wavelet transform mode of the signal corresponds to the local extreme point of the signal. The obtained abrupt change point can guide the identification of the waveform shape.
[0076] Step 3: Based on the design of the one-dimensional continuous wavelet transform algorithm, identify the waveform morphology;
[0077] First, let's explain the definition of "waveform": Waveforms are divided into "positive waveforms" and "negative waveforms," each with three characteristic values. The three characteristic values of a "positive waveform" are: two minimum values and one maximum value; the three characteristic values of a "negative waveform" are: two maximum values and one minimum value. A "positive waveform" is defined as a curve segment with exactly three extreme points, appearing in the order of minimum, maximum, and minimum. A "negative waveform" is defined as a curve segment with exactly three extreme points, appearing in the order of maximum, minimum, and maximum.
[0078] In practical applications, the abrupt change points are used as references to determine the waveform boundaries. Using formula (5) in the second step, the entire natural potential curve is traversed from top to bottom according to depth, and all abrupt change points of the entire curve are recorded to identify the waveform shape. Then, the maximum value points are found in each "positive waveform" and "negative waveform", and the magnitude of the natural potential curve value and the corresponding depth are recorded.
[0079] Step 4: Based on the identified waveform morphology, connect the mudstone baseline to construct the spontaneous potential curve mudstone baseline.
[0080] Mark the maximum value and depth location of each waveform of the spontaneous potential curve found in step three. Then, set a depth window (to avoid making the mudstone baseline connection too tortuous, it is recommended to choose a depth of 30 meters as a window length). Within each depth window, compare the maximum values of all spontaneous potential curve waveforms within the window, and find the maximum value among all waveform maxima, marking its location and corresponding spontaneous potential amplitude. Then, move the depth window down by a fixed step and repeat the above steps to ensure that the window covers the spontaneous potential curves within the entire depth segment. Finally, connect all the found maximum spontaneous potential values, and use linear interpolation to fill in the data between each maximum value to form a continuous curve, i.e., the spontaneous potential mudstone baseline.
[0081] Example 1
[0082] The method of the present invention will be further illustrated below using the Zhong 44-Jian P204 well as an example.
[0083] Figure 2 This is a diagram showing the interpretation results of the spontaneous potential (SP) mudstone baseline from well Zhong 44-Jian P204. From left to right, the first line is the depth line; the second is the oil layer group line; the third is the deep lateral resistivity (RLLD), shallow lateral resistivity (RLLS), and microsphere resistivity curve (RXO); the fourth is the micro-gradient resistivity (RMG) and micropotential curve (RMN); the fifth is the lithological density (DEN) and sonic logging curve (HAC); and the sixth is the SP curve and the SP mudstone baseline line. The specific calculation steps are as follows:
[0084] Step 1: Finding the first derivative of Gaussian
[0085] First, define the Gaussian function G(x), which has the following form:
[0086]
[0087] Where σ is the standard deviation of the Gaussian function, which determines the width of the function, m;
[0088] x is the independent variable, representing a certain depth point on the spontaneous potential curve, m.
[0089] Taking the first derivative of the Gaussian function G(x) yields the Gaussian first derivative function.
[0090]
[0091] Step 2: Design of the one-dimensional continuous wavelet transform algorithm
[0092] The natural potential curve SP can be considered as a signal SP(x). To perform wavelet transform, we need to use the first Gaussian derivative from the first step for scaling and translation, denoted as:
[0093]
[0094] Where: a is the scale factor. Since the natural potential curve requires processing of a large range of variation characteristics, the value of a can be taken to be larger. Here we take 0.2m.
[0095] b is the time shift factor, reflecting displacement, and here b takes a value of 0.05m;
[0096] x represents a depth point on the natural potential curve, m.
[0097] Then, the convolution of the spontaneous potential curve SP(x) with the scale-scaled translation function is calculated to obtain the wavelet coefficients W of the spontaneous potential logging curve. sp (a,b):
[0098]
[0099] Substituting formula (3) into formula (4), the wavelet coefficient W of the spontaneous potential logging curve sp The expression (a, b) is transformed into the following form:
[0100]
[0101] This is the specific calculation process of the wavelet coefficients of the natural potential curve SP(x) at scale a and location b.
[0102] Step 3: Identification of waveform shape.
[0103] Using formula (5) in the second step, the entire spontaneous potential curve is traversed from top to bottom according to depth, and all abrupt change points of the entire curve are recorded to identify the waveform shape. Then, the maximum value point is found in each "positive waveform" and "negative waveform" and the magnitude of the spontaneous potential curve value and the corresponding depth are recorded (see Appendix 1 for detailed data of the spontaneous potential mudstone baseline interpretation results of well Zhong 44-Jian P204. Due to the depth of the well, there are too many depth points, the amount of data is large, and the table is long. Appendix 1 only gives the data of a part of the well section).
[0104] Step 4: Connect the mudstone baseline.
[0105] Mark the maximum value and depth location of each waveform of the spontaneous potential curve found in step three. Then, set a depth window (here we take the window size as 30m). Within each depth window, compare the maximum values of all spontaneous potential curve waveforms within the window, and mark the location and corresponding spontaneous potential amplitude of the maximum value among all waveform maxima. Next, move the depth window downwards by a fixed step size and repeat the above steps to ensure that the window covers the spontaneous potential curves within the entire depth range. Finally, connect all the found maximum spontaneous potential values, and use linear interpolation to fill in the data between each maximum value, forming a continuous curve, i.e., the spontaneous potential mudstone baseline (see Appendix). Figure 2 ).
[0106] Table 1
[0107]
[0108]
[0109]
[0110] Example 2
[0111] Taking well Bei 1-Ding 5-Jian E48 as an example, the interpretation results of this well are shown in the figure below. Figure 3 .
[0112] Figure 3 This is a diagram showing the interpretation results of the spontaneous potential mudstone baseline of well Bei 1-Ding 5-Jian E48. The first line from the left is the depth line; the second line is the oil layer group line; the third line is the deep lateral resistivity curve (RLLD), shallow lateral resistivity curve (RLLS), and microsphere resistivity curve (RXO); the fourth line is the micro-gradient curve (RMG) and micropotential curve (RMN); the fifth line is the lithological density curve (DEN) and sonic curve (HAC); and the sixth line is the spontaneous potential curve and the spontaneous potential mudstone baseline line.
[0113] The specific calculations follow the same steps as in Example 1.
[0114] The embodiments described above are merely illustrative of implementation methods of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications, equivalent substitutions, and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.
Claims
1. A method for constructing a mudstone baseline for a spontaneous potential curve, characterized in that: Includes the following steps: S1: The algorithm of one-dimensional wavelet continuous transform is adopted, and the natural potential logging curve is transformed by one-dimensional continuous wavelet transform using the first derivative of Gauss as the basis wavelet. S2: Design of a one-dimensional continuous wavelet transform algorithm; S3: Based on the design of a one-dimensional continuous wavelet transform algorithm, waveform morphology is identified; S4: Based on the identified waveform morphology, connect the mudstone baseline to construct the natural potential curve mudstone baseline.
2. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 1, characterized in that: The method for obtaining the first Gaussian derivative of the spontaneous potential logging curve in step S1 includes the following steps: 2.1 First, define the Gaussian function G(x), which has the following form: Where: σ is the standard deviation of the Gaussian function, which determines the width of the function, m; x is the independent variable, representing a certain depth point on the spontaneous potential curve, m; 2.
2. Taking the first derivative of the Gaussian function G(x) yields the first derivative function of the Gaussian function for the spontaneous potential logging curve.
3. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 2, characterized in that: The specific method for designing the one-dimensional continuous wavelet transform algorithm in step S2 includes the following steps: 3.
1. Treat the natural potential curve SP as the signal SP(x); 3.
2. To perform wavelet transform, the Gaussian first derivative from step 2.2 is used for scaling and translation. The resulting function of the natural potential logging curve after scaling and translation is denoted as: Where: a is the scale factor, m; b is the time shift factor, reflecting displacement, m; x represents a depth point on the spontaneous potential curve, in meters (m). The function of spontaneous potential logging curve after scaling and translation; 3.3 Calculate the convolution of the spontaneous potential curve SP(x) with the function after scale-scaling translation to obtain the wavelet coefficients W of the spontaneous potential logging curve. sp (a,b): Substituting formula (3) into formula (4), the wavelet coefficient W sp The expression (a, b) is transformed into the following form: The above steps are the specific calculation process of the wavelet coefficients of the natural potential curve SP(x) at scale a and location b.
4. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 3, characterized in that: In step S2, when the wavelet function can be regarded as the first derivative of a certain smooth function, the abrupt change point or edge of the wavelet transform mode of the signal corresponds to the local extreme point of the signal. The obtained abrupt change point guides the identification of the waveform shape.
5. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 1, characterized in that: The waveform in step S3 is divided into positive waveform and negative waveform, and each waveform has 3 characteristic values; The three characteristic values of a positive waveform are: two minimum values and one maximum value; The three characteristic values of a negative waveform are: two maxima and one minima.
6. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 5, characterized in that: The positive waveform is defined as a curve segment with exactly three extreme points, which appear in sequence as minimum, maximum, and minimum.
7. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 5, characterized in that: The negative waveform is defined as a curve segment with exactly three extreme points, which appears in sequence as a maximum point, a minimum point, and another maximum point.
8. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 4, characterized in that: The method for identifying waveform morphology includes: In practical applications, the point of abrupt change is used as a reference for determining the waveform limit. Using the wavelet coefficients W of the spontaneous potential logging curve in step S3 sp (a,b) expression formula (5), traverse the entire natural potential curve from top to bottom according to the depth, record all the abrupt change points of the entire curve, and use all the abrupt change points of the entire natural potential curve to identify the waveform shape; Find the maximum point in each positive and negative waveform, and record the magnitude and depth of the natural potential curve corresponding to each maximum point.
9. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 8, characterized in that: The method for constructing the mudstone baseline of the spontaneous potential curve is as follows: The markings are based on the maximum points and depth locations corresponding to each waveform of the natural potential curve that have been found. Then, a depth window is set. Within each depth window, the maximum values of all natural potential curves within the window are compared, and the maximum value among all waveform maxima is found, its position and the corresponding natural potential amplitude are marked. The depth window is then moved down by a fixed step and the above steps are repeated to ensure that the window covers the spontaneous potential curve within the entire depth segment. Finally, all the found maximum spontaneous potential values are connected, and the data between each maximum value is filled in using linear interpolation to form a continuous curve, i.e., the spontaneous potential mudstone baseline.
10. The method for constructing a mudstone baseline for a spontaneous potential curve according to claim 9, characterized in that: The length of a depth window is set to 10-30m.