Target position optimization method and system based on ultra-deep fault control cave type reservoir thickness map

Abstract

The invention relates to the technical field of oil development, in particular to a target position optimization method and system based on an ultra-deep fault control cave type reservoir thickness map. Comprising the following steps: S1, compiling an ultra-deep fault control cave type reservoir thickness map, specifically comprising the following steps: S101, determining a detection attribute of a fault control cave type reservoir; s102, determining a threshold value of a fault control cave type reservoir detection attribute; s103, calculating the thickness value of each position in the fault-control cave type reservoir, specifically comprising the following steps: S104, according to the thickness value of the plane position, compiling a thickness distribution diagram of the fault-control cave type reservoir at the target area position through an isoline; s2, on the basis of the ultra-deep fault control cave type reservoir thickness map obtained in the step S1, the position with the maximum thickness value is set as a target position for well drilling control; according to the method, the thickness of the fault control cave type reservoir is calculated accurately, target position optimization can be rapidly and efficiently guided, and support is provided for fine evaluation of a fault control reservoir body and well position trajectory design.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum development, and in particular to a target position optimization method and system based on a thickness map of an ultra-deep fault-controlled cave reservoir. Background Art

[0002] The main reservoir type of strike-slip fault-controlled reservoirs is cave reservoirs. The thickness of fault-controlled cave reservoirs can intuitively show the degree of reservoir development. The thickness of fault-controlled cave reservoirs can guide the prediction of the development location of large-scale reservoirs and provide support for the design of well deployment targets to ensure the target scale reservoirs are hit. However, cave reservoirs are affected by the control of fault differences, the spatial distribution of reservoirs varies greatly, and they are strongly affected by spatial heterogeneity. It is difficult to accurately describe the thickness and quickly and efficiently guide the optimization of target locations.

[0003] Prior art CN115542399A discloses a method for predicting the thickness distribution of carbonate fracture-cavity reservoirs, comprising the following steps: (1) performing well seismic calibration on the drilled reservoirs of carbonate fracture-cavity reservoirs; (2) performing multi-sensitivity attribute analysis on different reservoirs, and characterizing the spatial distribution of the reservoirs in the time domain through machine learning; (3) converting the spatial distribution results in the time domain into a depth domain distribution using a velocity model, performing thickness projection calculations at different coordinate positions on the plane, and forming a reservoir thickness distribution plane map; (4) performing a relationship fitting between the actual drilled reservoir thickness at multiple sample points and the reservoir thickness data at the corresponding coordinates through machine learning, and correcting the reservoir thickness distribution plane map using a fitting algorithm. The velocity model selected in the above method is mostly established using offset velocity or high-frequency smoothed proportional velocity, resulting in low calculation accuracy for the thickness of fault-controlled cave reservoirs.

[0004] Therefore, there is an urgent need to provide a target location optimization method and system based on the thickness map of ultra-deep fault-controlled cave reservoirs. Summary of the Invention

[0005] The present invention solves the technical problems existing in the prior art and provides a target position optimization method and system based on a thickness map of ultra-deep fault-controlled cave reservoirs.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] The target location optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs includes the following steps:

[0008] S1. Compiling a thickness map of ultra-deep fault-controlled cave reservoirs, specifically including the following steps:

[0009] S101. Determine the detection attributes of fault-controlled cave reservoirs;

[0010] S102, determining a threshold value for detection attributes of fault-controlled cave reservoirs;

[0011] S103, calculating the thickness value of each position in the fault-controlled cave reservoir, specifically comprising the following steps:

[0012] S1031. Establishing a plane grid structure;

[0013] S1032. According to the threshold value of the fault-controlled cave reservoir detection attribute determined in step S102, extract the number of grids at the plane distribution positions where the detection attribute is greater than the threshold value;

[0014] S1033. Calculate the plane position thickness value of the target position according to the number of grids obtained in step S1032;

[0015] S104. Based on the thickness values ​​at the plane position, a thickness distribution map of the fault-controlled cave reservoir at the target area is prepared by using contour lines;

[0016] S2. Based on the thickness map of the ultra-deep fault-controlled cave reservoir obtained in step S1, the position with the maximum thickness value is set as the target position for drilling control.

[0017] Furthermore, in step S1033 , the thickness value is calculated by the following formula: thickness value = number of grids at the target position × time sampling rate × layer velocity.

[0018] Furthermore, the time sampling rate is determined by seismic data acquisition and time domain processing parameters.

[0019] Furthermore, the layer velocity is specifically calculated by the following formula: layer velocity = layer thickness ÷ propagation time.

[0020] Furthermore, S101 specifically includes the following steps:

[0021] S1011. Determine the seismic reflection characteristics of cave-like reservoirs controlled by strike-slip faults;

[0022] S1012. Based on S1011 and combined with the fine calibration of the drilled wells, the matching rate between the spatial distribution of the drilled cave reservoir and different detection attributes is obtained, and the detection attribute whose matching rate with the spatial distribution of the drilled cave reservoir is greater than the set value is selected as the detection attribute of the fault-controlled cave reservoir.

[0023] Furthermore, the seismic reflection characteristics in step S1011 are determined based on the location of Class I cave reservoirs or drilling emptying and severe loss of return and loss according to the well logging interpretation of the well, and are determined to be strong energy "beaded" anomalies under the control of the fault zone.

[0024] Furthermore, the set value is set to 83%.

[0025] Furthermore, the detection attribute of fault-controlled cave reservoirs is instantaneous energy.

[0026] Furthermore, S102 specifically includes the following steps:

[0027] S1021. Detailed description of the spatial relationship between cave reservoirs and instantaneous energy;

[0028] S1022. Determine the threshold value of instantaneous energy of fault-controlled cave reservoirs by setting a method.

[0029] Furthermore, the specific method of S1021 is: based on the comprehensive data of well logging and core data of drilled wells, combined with the emptying and leakage positions of well logging, and then through the acoustic wave time difference and density in the comprehensive logging curve, the depth-time correspondence of drilling-seismic data is calibrated and analyzed to obtain a fine description of the spatial position relationship between cave reservoirs and instantaneous energy.

[0030] Furthermore, the setting method in step S1022 is: the instantaneous energy anomaly boundary amplitude of the position where the set condition occurs in the drilled well is the threshold value of the instantaneous energy of the fault-controlled cave reservoir.

[0031] Furthermore, the setting conditions are: simultaneously satisfying that the well has encountered a Class I cave reservoir in the well logging, and that the well has encountered a cave reservoir with loss of return.

[0032] Furthermore, the threshold value of instantaneous energy for cave-type reservoirs is 87.

[0033] The target position optimization system based on the ultra-deep fault-controlled cave reservoir thickness map includes a first module and a second module. The first module is used to execute the content in step S1, and the second module is used to execute the content in step S2.

[0034] Furthermore, the first module includes a third module, a fourth module, a fifth module and a sixth module connected in sequence, the third module is used to execute the content in step S101, the fourth module is used to execute the content in step S102, the fifth module is used to execute the content in step S103, and the sixth module is used to execute the content in step S104.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) The present invention realizes thickness description, thickness map compilation and target position optimization by optimizing the detection attributes of fault-controlled cave reservoirs, determining the detection attribute threshold value of fault-controlled cave reservoirs based on drilling calibration, and calculating the thickness of fault-controlled cave reservoirs. The average data accuracy of the fine calculation of the completed drilling is high, and the thickness of the fault-controlled cave reservoir is calculated more accurately, which provides support for the fine evaluation of fault-controlled reservoirs and the design of well trajectory.

[0037] (2) The present invention makes full use of seismic data constraints to reduce the limited impact of actual drilling reservoir constraints on the correction of reservoir thickness at the wellbore under complex geological background. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the method of the present invention.

[0039] Figure 2 It is a schematic diagram of the seismic data of Well 1 in Shunbei area of ​​the present invention.

[0040] Figure 3 It is a schematic diagram of the instantaneous energy attributes of the superimposed seismic data of Well 1 in Shunbei area of ​​the present invention.

[0041] Figure 4 It is a schematic diagram of the instantaneous energy attributes of the superimposed seismic data at Wells 1 and 2 in the Shunbei area of ​​the present invention.

[0042] Figure 5 It is a schematic diagram of the depth and thickness map of the cave reservoir of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all 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.

[0044] like Figure 1 As shown, the present invention provides a target position optimization method based on an ultra-deep fault-controlled cave reservoir thickness map, comprising the following steps:

[0045] S1. Compiling a thickness map of ultra-deep fault-controlled cave reservoirs, specifically including the following steps:

[0046] S101, determining the detection attributes of the fault-controlled cave reservoir, specifically comprising the following steps:

[0047] S1011. Based on the well logging interpretation of 43 drilled wells in the Shunbei area, Class I cave reservoirs or locations of drilling blowouts and severe loss of return, the seismic reflection characteristics of cave reservoirs controlled by strike-slip faults were determined to be strong energy "beaded" anomalies controlled by fault zones;

[0048] S1012. Based on S1011 and in combination with the fine calibration of the drilled wells, obtain the coincidence rate between the spatial distribution of the drilled cave reservoir and different detection attributes, and select the detection attribute whose coincidence rate with the spatial distribution of the drilled cave reservoir is greater than a set value as the fault-controlled cave reservoir detection attribute;

[0049] Specifically, through the drilling of wells such as Well 1 and Well 2 in the fault-controlled reservoir group in Shunbei area, it was found that the consistency rate between cave reservoirs and instantaneous energy was 83%, so the set value was set to 83%, and instantaneous energy was set as the detection attribute of fault-controlled cave reservoirs.

[0050] S102, determining a threshold value for fault-controlled cave reservoir detection attributes based on well calibration, specifically comprising the following steps:

[0051] S1021. Detailed description of the spatial relationship between cavernous reservoirs and instantaneous energy. The specific method is: Based on comprehensive logging and core data from 43 wells drilled in the Shunbei area, combined with well logging blowdown and leakage locations, and using the acoustic wave delay and density in the comprehensive logging curves, a calibration analysis of the depth-time correspondence between drilling and seismic data was performed to obtain a detailed description of the spatial relationship between cavernous reservoirs and instantaneous energy.

[0052] S1022. Determine a threshold value for the instantaneous energy of a fault-controlled cavernous reservoir by a setting method. The setting method is as follows: the instantaneous energy anomaly boundary amplitude at the location where the set condition occurs in the drilled well is used as the threshold value for the instantaneous energy of the fault-controlled cavernous reservoir. The setting condition is that the well has encountered a Class I cavernous reservoir in the well logging, and a loss of return occurs when drilling encounters a cavernous reservoir.

[0053] Specifically, take the wells 1 and 2 that have encountered fault-controlled reservoirs and cavernous reservoirs as an example. Figure 2 、 Figure 3 、 Figure 4 As shown, Well 1 encountered a Class I cave reservoir in the well logging at the position where the instantaneous energy anomaly boundary amplitude was 87 at 7999 m, and the drilling at this position caused loss of return and leakage; Well 2 encountered a Class I cave reservoir in the well logging at the position where the instantaneous energy anomaly boundary amplitude was 87, and the drilling at this position caused loss of return and leakage, so the threshold value of the instantaneous energy of the cave reservoir is 87.

[0054] S103, calculating the thickness value of each position of the target position, specifically including the following steps:

[0055] S1031. Based on the seismic data bins determined by the acquisition and processing parameters of the target area, a grid structure of the fault-controlled reservoir cave-type reservoir plane in the area is established, wherein the grid structure is consistent with the seismic data bins in the area;

[0056] S1032. Using the threshold value of the fault-controlled cave reservoir detection attribute determined in step S2 and combining it with the three-dimensional spatial attribute volume of instantaneous energy, extract the number of grid cells at each plane distribution position where the amplitude of the instantaneous energy anomaly boundary is greater than the threshold value 87;

[0057] S1033. Calculate the thickness value of the target position, specifically by the following formula:

[0058] Thickness value = number of grids at target position × time sampling rate × layer velocity

[0059] The time sampling rate is determined by the parameters of seismic data acquisition and time domain processing. The time sampling rate in Shunbei area is 2ms. The interval velocity is calculated based on the time-depth relationship of the actual drilling of adjacent wells, and is specifically calculated using the following formula:

[0060] Layer velocity = layer thickness ÷ propagation time

[0061] S104. According to the thickness values ​​with different plane positions, a thickness distribution map of the fault-controlled cave reservoir at the target area is compiled through contour lines (such as Figure 5 shown).

[0062] S2. Based on the thickness map of the ultra-deep fault-controlled cave reservoir obtained in step S1, the position with the maximum thickness value is set as the drilling control target position.

[0063] The present invention also provides a target position optimization system based on an ultra-deep fault-controlled cave reservoir thickness map, comprising a first module and a second module, wherein the first module is used to execute the contents of step S1, and the second module is used to execute the contents of step S2; the first module also includes a third module, a fourth module, a fifth module, and a sixth module, and the third module, the fourth module, the fifth module, and the sixth module are connected in sequence, the third module is used to execute the contents of step S101, the fourth module is used to execute the contents of step S102, the fifth module is used to execute the contents of step S103, and the sixth module is used to execute the contents of step S104.

[0064] The present invention provides support for the detailed evaluation of fault-controlled cave reservoirs and the design of well trajectory by optimizing the detection attributes of fault-controlled cave reservoirs, determining the detection attribute threshold of fault-controlled cave reservoirs based on drilling calibration, and calculating the thickness of fault-controlled cave reservoirs to achieve thickness description and thickness map compilation. Compared with the velocity models selected in the prior art, which are mostly established using offset velocity or proportional velocity after high-frequency smoothing, the present invention uses the average data of fine calculations of completed wells with higher accuracy and calculates the thickness of fault-controlled cave reservoirs more accurately. Compared with the prior art, which uses the relationship fitting of the actual drilled reservoir thickness at multiple sample points with the reservoir thickness data at the corresponding coordinates to achieve the correction of the reservoir thickness distribution plane, the present invention fully utilizes the constraints of seismic data to reduce the limited impact of the correction of reservoir thickness by the actual drilled reservoir constraints at the wellbore under complex geological backgrounds (such as well logging, drilling, etc. revealing that the reservoir thickness is small or absent, but the well productivity is high).

[0065] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A target location optimization method based on a thickness map of ultra-deep fault-controlled cave reservoirs, characterized by: The following steps are involved: S1. Compiling a thickness map of ultra-deep fault-controlled cave reservoirs, specifically including the following steps: S101. Determine the detection attributes of fault-controlled cave reservoirs; S102, determining a threshold value for detection attributes of fault-controlled cave reservoirs; S103, calculating the thickness value of each position in the fault-controlled cave reservoir, specifically comprising the following steps: S1031. Establishing a plane grid structure; S1032. According to the threshold value of the fault-controlled cave reservoir detection attribute determined in step S102, extract the number of grids at the plane distribution positions where the detection attribute is greater than the threshold value; S1033. Calculate the plane position thickness value of the target position according to the number of grids obtained in step S1032; S104. Based on the thickness values ​​at the plane position, a thickness distribution map of the fault-controlled cave reservoir at the target area is prepared by using contour lines; S2. Based on the thickness map of the ultra-deep fault-controlled cave reservoir obtained in step S1, the position with the maximum thickness value is set as the target position for drilling control.

2. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 1 is characterized in that: In step S1033, the thickness value is calculated by the following formula: thickness value = number of grids at the target position × time sampling rate × layer velocity.

3. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 2 is characterized in that: The time sampling rate is determined by seismic data acquisition and time domain processing parameters.

4. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 2 is characterized in that: The layer velocity is calculated by the following formula: layer velocity = layer thickness ÷ propagation time.

5. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 1 is characterized in that: S101 specifically includes the following steps: S1011. Determine the seismic reflection characteristics of cave-like reservoirs controlled by strike-slip faults; S1012. Based on S1011 and combined with the fine calibration of the drilled wells, the matching rate between the spatial distribution of the drilled cave reservoir and different detection attributes is obtained, and the detection attribute whose matching rate with the spatial distribution of the drilled cave reservoir is greater than the set value is selected as the detection attribute of the fault-controlled cave reservoir.

6. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 5 is characterized in that: The seismic reflection characteristics in step S1011 are determined based on the Class I cave reservoir or drilling emptying and severe loss of return and leakage interpretation of the well, and are determined to be strong energy "beaded" anomalies controlled by the fault zone.

7. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 5 is characterized in that: The setpoint is set to 83%.

8. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 5 is characterized in that: The detection attribute of fault-controlled cave reservoirs is instantaneous energy.

9. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 1 is characterized in that: S102 specifically includes the following steps: S1021. Detailed description of the spatial relationship between cave reservoirs and instantaneous energy; S1022. Determine the threshold value of instantaneous energy of fault-controlled cave reservoirs by setting a method.

10. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 9 is characterized in that: The specific method of S1021 is: based on the comprehensive data of well logging and core data of drilled wells, combined with the locations of emptying and leakage in well logging, and then through the acoustic wave time difference and density in the comprehensive logging curve, a calibration analysis of the depth-time correspondence of drilling-seismic data is carried out to obtain a fine description of the spatial position relationship between cave reservoirs and instantaneous energy.

11. The target position optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 9 is characterized in that: The setting method in step S1022 is: the instantaneous energy anomaly boundary amplitude at the location where the set condition occurs in the drilled well is the threshold value of the instantaneous energy of the fault-controlled cave reservoir.

12. The target location optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 11 is characterized in that: The setting conditions are: the drilling has encountered a Class I cave reservoir in the well logging, and the drilling has encountered a cave reservoir with loss of return.

13. The target location optimization method based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 9 is characterized in that: The threshold value of instantaneous energy of cave reservoirs is 87.

14. A system using the target position optimization method based on the ultra-deep fault-controlled cave reservoir thickness map according to any one of claims 1 to 13, characterized in that: It includes a first module and a second module, wherein the first module is used to execute the content in step S1, and the second module is used to execute the content in step S2.

15. The target position optimization system based on the thickness map of ultra-deep fault-controlled cave reservoirs according to claim 14, characterized in that: The first module includes a third module, a fourth module, a fifth module and a sixth module connected in sequence, the third module is used to execute the content in step S101, the fourth module is used to execute the content in step S102, the fifth module is used to execute the content in step S103, and the sixth module is used to execute the content in step S104.

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