Horizontal well horizontal section segmentation method based on synthetic productivity curve power segmentation

Abstract

The invention discloses a horizontal well horizontal section segmentation method based on synthetic productivity curve power segmentation, which comprises the following steps of: establishing a function relational expression between permeability and natural gamma and resistivity curves by using logging data; calculating a permeability distribution curve along the well body direction of the horizontal well by utilizing the function relation; forming a synthetic curve representing the productivity by using the permeability curve; detecting extreme points of the synthetic curve, and merging and sorting the extreme points and the sequence data points to serve as input data; and based on the segmentation points, a power segmentation technology is applied to carry out self-adaptive segmentation on the synthetic curve, and then segments of the horizontal well are obtained. According to the method, subjective deviation caused by manual setting of a segmentation threshold value is eradicated, self-adaptive segmentation is achieved and is independent of a method for directly measuring permeability or porosity data, the problem of data missing in traditional horizontal well development is effectively solved, and meanwhile, through the self-adaptive synthetic curve segmentation technology, the development efficiency of the horizontal well is improved. And the precision and efficiency of the analysis and optimization process of the horizontal well are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas field exploitation, and particularly relates to a horizontal well horizontal section segmentation method based on power division of synthetic productivity curves. Background Art

[0002] During the development of horizontal well oil and gas fields, due to the complex and variable underground geological conditions, the productivity of oil and gas reservoirs often shows unevenness in spatial distribution. In order to improve the recovery efficiency of oil and gas, it is usually necessary to accurately segment the horizontal well and apply different degrees of stimulation measures such as fracturing to each segment.

[0003] Traditionally, the segmentation of horizontal wells usually relies on well logging interpretations of permeability or porosity. The above data is crucial for optimizing the development of horizontal wells and improving productivity. However, in the case of only GR (natural gamma) and RT (resistivity) well logging curves or other similar situations, the above data is not available. Therefore, a method is needed to effectively segment horizontal wells using existing well logging data. Summary of the Invention

[0004] The problem to be solved by the present invention is to provide a horizontal well horizontal section segmentation method based on power division of synthetic productivity curves, which can more accurately identify the segmentation points of horizontal wells, thereby effectively improving the development efficiency and productivity of oil and gas fields.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: a horizontal well horizontal section segmentation method based on power division of synthetic productivity curves, comprising the following steps,

[0006] S1: Using the well logging data of at least one surrounding well, establish a functional relationship between permeability and natural gamma and resistivity curves;

[0007] S2: Apply the functional relationship to the natural gamma curve and density curve data of the horizontal well, and calculate the permeability distribution curve along the horizontal wellbore direction;

[0008] S3: Use the permeability curve to form a synthetic curve representing productivity;

[0009] S4: Detect the extreme points of the synthetic curve, and merge and sort the extreme points and the sequence data points as input data;

[0010] S5: Based on the segmentation points determined in S4, apply power division technology to adaptively segment the synthetic curve, and then obtain the segmentation of the horizontal well.

[0011] Furthermore, in S1, the well logging data of the surrounding well includes but is not limited to permeability, porosity, natural gamma and resistivity curve data.

[0012] Further, in the step S1, the functional relationship is as follows:

[0013]

[0014] In the formula: K(i) is the permeability at the inclination point i, with the unit of md; RT(i) is the resistivity at the inclination point i, in ohm-m; GR(i) is the natural gamma ray at the inclination point i, in API (gamma ray unit); where a 1 , a 2 , a 3 , a 4 , a 5 are undetermined coefficients.

[0015] Further, in the step S3, the permeability curve is combined with the formation effective thickness, fluid viscosity, outer boundary radius, and wellbore radius to form a synthetic curve representing the productivity.

[0016] Further, in the step S3, the formula of the synthetic curve is as follows:

[0017]

[0018] In the formula: h(i) is the formation thickness at the inclination point i, with the unit of m; μ(i) is the fluid viscosity at the inclination point i, in Pa·s; re(i) is the outer boundary radius at the inclination point i, in m; rw(i) is the wellbore radius at the inclination point i, in m.

[0019] Further, the step S5 includes the following steps:

[0020] S51: Scan the synthetic curve and mark the peak points and valley points of the synthetic curve;

[0021] S52: Sort the peak points and valley points according to their positions on the curve to form an array;

[0022] S53: Identify the initial segmentation point of the array, and the initial segmentation point divides the array into two subintervals;

[0023] S54: Repeat the segmentation point operation of the step S53 for the two subintervals to further subdivide each subinterval, and this process is recursively executed until the required number of segments with a power of two is completed;

[0024] S55: Sort the integral mean differences of the segments from large to small, and determine and return the preset number of segments and the positions of the segmentation points.

[0025] Further, in the step S53, the initial segmentation point divides the array into two sub-intervals, maximizing the integral mean values of the two sub-intervals.

[0026] Further, the present invention provides a device for running the above data processing method.

[0027] Further, the present invention provides a device, including a memory, a processor, and an algorithm stored in the memory and executable on the processor. When the processor executes the computer program, the above data processing method is implemented.

[0028] Further, the present invention provides a computer-readable storage medium storing a computer algorithm, which implements the above data processing when executed by a processor.

[0029] The advantages and positive effects of the present invention are as follows:

[0030] The present invention eliminates the subjective deviation of manually setting the segmentation threshold and realizes adaptive segmentation. The present invention is independent of the method of directly measuring permeability or porosity data, effectively overcomes the problem of data loss faced by traditional horizontal well development, and at the same time, through the adaptive synthetic curve segmentation technology, greatly improves the accuracy and efficiency of the horizontal well analysis and optimization process. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a schematic diagram of the overall process of an embodiment of the present invention.

[0032] Figure 2 is a diagram of permeability and porosity data of a specific embodiment of the present invention.

[0033] Figure 3 is a diagram of the fitting linear regression equation of permeability and porosity of a specific embodiment of the present invention.

[0034] Figure 4 is a schematic diagram of the fitting of the original data and the curve GR of a specific embodiment of the present invention.

[0035] Figure 5 is a schematic diagram of the synthetic curve of a specific embodiment of the present invention.

[0036] Figure 6 is a schematic diagram of the marking of the peak points and valley points of the synthetic curve of a specific embodiment of the present invention.

[0037] Figure 7 is a schematic diagram of the segmentation of the synthetic curve of a specific embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] The following further describes the embodiments of the present invention in conjunction with the accompanying drawings:

[0040] As Figure 1 shown, a horizontal well horizontal section segmentation method based on power division of synthetic productivity curves includes the following steps.

[0041] S1: Using the logging data of at least one surrounding well, where the logging data of the surrounding well includes, but is not limited to, permeability, porosity, natural gamma, and resistivity curve data, establish a functional relationship between permeability and natural gamma and resistivity curves. The functional relationship is as follows,

[0042]

[0043] In the formula: K(i) is the permeability at the inclination point i, unit md; RT(i) is the resistivity at the inclination point i, ohm-meter (ohm-m); GR(i) is the natural gamma ray at the inclination point i, gamma ray unit (API); where a 1 , a 2 , a 3 , a 4 , a 5 are undetermined coefficients. Substitute RT, GR, and K to obtain the undetermined coefficients, and then the functional relationship between permeability and GR and RT curves can be obtained.

[0044] S2: Apply the functional relationship to the natural gamma ray and density curve data of the horizontal well to calculate the permeability distribution curve along the horizontal wellbore direction.

[0045] S3: Combine the permeability curve with the formation effective thickness, fluid viscosity, outer boundary radius, and wellbore radius to form a synthetic curve representing productivity. The synthetic curve formula is as follows,

[0046]

[0047] In the formula: h(i) is the formation thickness at the inclination point i, unit m; μ(i) is the fluid viscosity at the inclination point i, unit Pa·s; re(i) is the outer boundary radius at the inclination point i, unit m; rw(i) is the wellbore radius at the inclination point i, unit m.

[0048] S4: Detect the extreme points of the synthetic curve, merge and sort the extreme points and the sequence data points, and use them as input data.

[0049] S5: Based on the segmentation points determined in S4, apply the power segmentation technique to adaptively segment the synthetic curve, and then obtain the segments of the horizontal well. Specifically, S5 includes the following steps:

[0050] S51: Scan the synthetic curve and mark the peak points and valley points of the synthetic curve.

[0051] S52: Sort the peak points and valley points according to their positions on the curve to form an array.

[0052] S53: Identify the initial segmentation point of the array. The initial segmentation point divides the array into two sub-intervals to maximize the integral mean values of the two sub-intervals.

[0053] S54: Repeat the segmentation point operation of S53 for the two sub-intervals to further subdivide each sub-interval. This process is recursively executed until the required power-of-two segmentation is completed.

[0054] S55: Sort the integral mean value differences of the segments from large to small, and determine and return the preset number of segments and the positions of the segmentation points.

[0055] The present invention will be specifically described below in conjunction with specific embodiments:

[0056] S1: Data acquisition and processing. Specifically, collect the logging data of at least one surrounding well, including permeability, porosity, natural gamma (GR), resistivity (RT), etc. Use the above data to establish a functional relationship between permeability and natural gamma and resistivity curves. Among them, the functional relationship is as follows:

[0057]

[0058] Formalized as the functional relationship K(i) = f(RT(i), GR(i)).

[0059] As Figure 2 shown, according to the permeability data in this embodiment, take the logarithm of permeability and porosity. As Figure 3 shown, and perform a linear regression equation to obtain Y = 9.68X + 18.82. Then a 1 = 9.68, a 2 = 18.82, the principle is the same as formula 1. Take the logarithm of the RT curve, combine it with the GR curve and substitute it into formula 1. As Figure 4 shown, fit to obtain the undetermined coefficients therein. Obtain a 3 = 0.0350, a 4 = 0.0017, a 5= 0.3242. Substitute these coefficients into Equation 1 to obtain the relationship between the GR, RT curves and permeability.

[0060] S2: Apply the functional relationship to the natural gamma ray and density curve data of horizontal wells to calculate the permeability distribution curve along the horizontal wellbore.

[0061] S3: Substitute the GR and RT logging data of the horizontal section of the horizontal well into Equation 1 to obtain the depth distribution of permeability, and at the same time substitute it into Equation (2) to generate the synthetic curve HC, as Figure 5 shown. Among them, the formula for the synthetic curve is as follows,

[0062]

[0063] S4: Detect the extreme points of the synthetic curve, merge and sort the extreme points and the sequence data points as the input data.

[0064] S5: Based on the segmentation points determined in S4, apply the power segmentation technique to adaptively segment the synthetic curve, and then obtain the segmentation of the horizontal well. Specifically, S5 includes the following steps.

[0065] S51: Scan the synthetic curve, and mark the peak points and valley points of the synthetic curve by judging the sizes of the points on the left and right of a single point, as Figure 6 shown.

[0066] S52: Sort the peak points and valley points according to their positions on the curve to form an array.

[0067] S53: Apply the power segmentation technique to identify the initial segmentation point of the array. The initial segmentation point divides the array into two sub-intervals to maximize the integral means of the two sub-intervals.

[0068] S54: Repeat the segmentation point operation of S53 for the two sub-intervals to further subdivide each sub-interval. This process is recursively executed until the required number of binary power segmentations is completed.

[0069] S55: Sort the differences in integral means of the segments from large to small, determine and return that the preset number of segments is 5, then return the positions of the four segmentation points from large to small, as Figure 7 shown. According to the identified segmentation points, perform actual segmentation operations on the horizontal well, so as to achieve more accurate oil and gas production enhancement measures.

[0070] The advantages and positive effects of the present invention are:

[0071] The present invention eliminates the subjective deviation of manually setting the segmentation threshold and realizes adaptive segmentation. The present invention is independent of the method of directly measuring permeability or porosity data, effectively overcomes the problem of data loss faced by traditional horizontal well development, and at the same time, through the adaptive synthetic curve segmentation technology, greatly improves the accuracy and efficiency of the horizontal well analysis and optimization process.

[0072] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the patent coverage scope of the present invention.

Claims

1. A horizontal well horizontal segmentation method based on power segmentation of synthetic production curve, characterized by: The following steps are included: S1: using the logging data of at least one surrounding well, establishing a functional relationship between permeability and natural gamma and resistivity curves; S2: The functional relationship is applied to the natural gamma ray and density curve data of the horizontal well to calculate the permeability distribution curve along the wellbore direction of the horizontal well; S3: using the permeability curve, forming a synthetic curve representing the capacity; S4: Detecting extreme points of the synthetic curve, merging and sorting the extreme points and the sequence data points as input data; S5: Based on the segmentation points determined in S4, the power segmentation technology is used to adaptively segment the synthetic curve, thereby obtaining the segmentation of the horizontal well.

2. A horizontal well horizontal segmentation method based on power segmentation of synthetic production curve according to claim 1, characterized in that: In S1, the logging data of the surrounding wells include but are not limited to permeability, porosity, natural gamma and resistivity curve data.

3. A horizontal well horizontal segmentation method based on power segmentation of synthetic production curve according to claim 1 or 2, characterized in that: In S1, the functional relationship is as follows: Where: K(i) is the permeability at the i-th point measured at the inclination, in md; RT(i) is the resistivity at the i-th point measured at the inclination, in ohm-meter (ohm-m); GR(i) is the natural gamma ray at the i-th point measured at the inclination, in gamma ray units (API); a1, a2, a3, a4, and a5 are unknown coefficients.

4. A horizontal well horizontal segmentation method based on power segmentation of synthetic productivity curve according to claim 1 or 2, characterized in that: In S3, the permeability curve is combined with the effective thickness of the formation, the fluid viscosity, the outer boundary radius and the wellbore radius to form a synthetic curve representing the production capacity.

5. A horizontal well horizontal segmentation method based on power segmentation of synthetic productivity curve according to claim 4, characterized in that: In S3, the synthetic curve formula is as follows: Where: h(i) is the formation thickness at the i-th point, in m; μ(i) is the fluid viscosity at the i-th point, in Pa·s; re(i) is the outer boundary radius at the i-th point, in m; rw(i) is the wellbore radius at the i-th point, in m.

6. A horizontal well horizontal segmentation method based on power segmentation of synthetic productivity curve according to claim 1 or 2, characterized in that: The S5 comprises the following steps: S51: Scan the synthetic curve and mark the peak points and trough points of the synthetic curve; S52: sorting the peak points and the trough points according to their positions on the curve to form an array; S53: Identify an initial segmentation point of the array, where the initial segmentation point divides the array into two sub-intervals; S54: Repeat the segmentation point operation of S53 for the two sub-intervals to further subdivide each sub-interval. This process is recursively performed until the required quadratic segmentation is completed. S55: Sort the integral mean differences of the segments from large to small, determine and return the preset number of segments and the positions of the segmentation points.

7. A horizontal well horizontal segmentation method based on power segmentation of synthetic production curve according to claim 6, characterized in that: In S53, the initial segmentation point divides the array into two sub-intervals so as to maximize the integral mean of the two sub-intervals.

8. A device, characterized in that: Run the data processing method according to any one of claims 1 to 7.

9. A device comprising a memory, a processor, and an algorithm stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data processing method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer algorithm, characterized in that: When the computer algorithm is executed by a processor, the data processing according to any one of claims 1 to 7 is implemented.

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