A method for optimizing advanced drilling parameters based on a two-factor model of porosity and water cut.

Abstract

This invention discloses a method for optimizing advanced drilling parameters based on a porosity-water cut dual-factor approach. First, geophysical exploration is used to obtain the porosity and water cut distributions of unexposed areas. Then, combined with historical borehole data, a three-dimensional physical property matrix of the unexposed areas is constructed, generating a three-dimensional continuous spatial distribution model. Next, based on the entropy weight-ideal point method, engineering levels are classified according to the porosity and water cut at different locations, and the correction values ​​for each drilling parameter correction item under each level are determined. Combining the three-dimensional continuous spatial distribution model with the engineering level classification, when drilling reaches a certain position, the corresponding drilling parameter correction item is invoked according to the engineering level of that position to correct the current drilling parameters, and drilling continues according to the corrected parameters. This invention, by fusing multi-source geophysical data to construct a porosity-water cut dual-factor collaborative driving model, achieves real-time correction of drilling parameters, promoting the upgrade of intelligent mining from passive response to active control.

Description

Technical Field

[0001] This invention belongs to the field of mine borehole detection technology, specifically a method for optimizing advanced drilling parameters based on a dual factor of porosity and water cut. Background Technology

[0002] In underground directional drilling projects in coal mines, precise control of drilling parameters is a core technology for achieving efficient water exploration and drainage and ensuring construction safety. As the mining depth increases, the dynamic coupling characteristics of porosity and water cut in the formation become increasingly significant. Especially under complex geological conditions such as concealed water-bearing structures and fractured zones, traditional drilling methods rely on static geological parameters and manual experience to adjust drilling pressure, rotation speed, and mud parameters, making it difficult to respond in real-time to changes in formation properties. This leads to low drilling efficiency in hard rock formations, frequent borehole instability in water-rich sections, and even induces water inrush accidents, seriously threatening safe mine production.

[0003] Currently, while geophysical exploration technologies (such as seismic wave exploration and electrical resistivity tomography) can acquire multi-source formation data in advance, existing drilling parameter control methods have the following problems: First, the interpretation accuracy of geophysical data is insufficient, and a single physical property parameter is difficult to characterize the synergistic mechanism of porosity and water cut; second, parameter correction is lagging, and the lack of closed-loop control logic based on dynamic physical property classification limits the adaptive capability of the drilling system. For example, in areas with concealed water-conducting channels, traditional methods cannot optimize the grouting process according to real-time water cut changes, which can easily lead to sealing failure; and in emergency rescue scenarios, drilling trajectory adjustment relies on manual experience, making it difficult to quickly construct life-saving channels.

[0004] Therefore, the research direction of this invention is to provide a new method for optimizing drilling parameters, which can be achieved by constructing a porosity-water content dual-factor collaborative driving model by integrating multi-source geophysical data, and realize real-time correction of drilling parameters to solve the technical problem of difficulty in balancing drilling efficiency and safety in complex formations. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method for optimizing advanced drilling parameters based on a porosity-water content dual-factor model. By integrating multi-source geophysical data to construct a porosity-water content dual-factor collaborative driving model, the drilling parameters can be corrected in real time, promoting the upgrade of intelligent mining from passive response to active control.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for optimizing advanced drilling parameters based on a dual factor of porosity and water cut, comprising the following steps:

[0007] Step 1: Multi-source geophysical data acquisition and 3D physical property modeling:

[0008] ① First, obtain seismic wave data and electrical resistivity data of the unexposed area in front of the tunnel face through geophysical exploration, and then obtain the porosity distribution and water content distribution of the unexposed area based on the seismic wave data and electrical resistivity data respectively;

[0009] ② Construct a three-dimensional physical property matrix of the unexposed area in front of the drilling face by combining historical drilling data;

[0010] ③ Combine the porosity distribution and water content distribution from step ① with the three-dimensional physical property matrix from step ② to generate a three-dimensional continuous spatial distribution model, and mark the risk and anomaly areas;

[0011] Step 2: Porosity-water cut classification and determination of drilling parameter correction items:

[0012] I. Based on the Entropy Weight-Ideal Point Method (EW-TOPSIS), porosity and water content are classified into engineering grades. The porosity and water content weights are dynamically generated through information entropy, and then different locations in the unexposed area are classified into different engineering grades.

[0013] II. Select multiple drilling parameter correction items, and determine the correction value of each drilling parameter correction item at each level according to the different engineering levels divided in step I.

[0014] Step 3: Drilling Rig Parameter Coordination Correction Strategy

[0015] A. Combine the three-dimensional continuous spatial distribution model from step one with the engineering grade classification from step two to determine the engineering grade corresponding to different positions in the model. When the drilling rig drills to a certain position, the drilling parameter correction item corresponding to step two is called to correct the current drilling parameters according to the engineering grade of that position.

[0016] B. Transmit the corrected drilling parameters to the drilling rig so that it can continue drilling at the current position according to the corrected drilling parameters;

[0017] Step 4: Directional drilling exploration of abnormal areas:

[0018] (i) Based on the risk anomaly areas marked in step one, plan the directional drilling trajectory (such as azimuth and dip angle);

[0019] (ii) The borehole orientation is adjusted in real time using measurement while drilling (MWD) technology, and the drilling parameters are corrected by repeating step three according to the engineering level of different locations the drilling passes through, so as to ensure accurate arrival at the target area;

[0020] (iii) During the drilling process, the three-dimensional continuous spatial distribution model generated in step one is dynamically verified based on the measured geological data (such as the amount of cuttings returned and the change in torque). If the requirements are not met, step one is repeated to regenerate the three-dimensional continuous spatial distribution model.

[0021] Furthermore, step I specifically comprises:

[0022] Entropy weighting method: quantifies the dispersion of data through information entropy, and assigns high weight to parameters with high dispersion.

[0023] Ideal point method: Calculate the closeness C between the parameter value and the optimal (safe) / worst (risk) solution to achieve continuous hierarchical classification;

[0024]

[0025] Specific steps:

[0026] (1) Data standardization and normalization:

[0027] Porosity normalization:

[0028] Moisture content normalization:

[0029]

[0030] (2) Calculate information entropy and entropy weight;

[0031]

[0032] (3) Define ideal solution: positive and negative ideal solutions correspond to the optimal safe working condition and the worst risk working condition, respectively;

[0033] (4) Calculate the application progress C.

[0034] (5) Single-factor classification; using progress tracking for classification;

[0035] (6) Engineering grade classification: The dual-factor grade classification is coupled, compressed, and eliminates contradictory combinations and merges equivalent risk combinations;

[0036] The risk gradient within the engineering level is refined using a weighted formula R; the initial coefficients are obtained through empirical formulas and change continuously as the project progresses.

[0037] R = α·φ norm +β·S w,norm +γ·(φ·S w )

[0038] Where α = 0.6, representing the weight of the normalized porosity value; β = 0.4, representing the weight of the normalized moisture content value; γ = 0.3, representing the coupling effect penalty coefficient; φ norm , representing normalized porosity; S w,norm φ·S represents the normalized moisture content. w It represents the product of the original porosity and the water content; finally, the engineering grade is divided into seven levels based on the R value.

[0039] Furthermore, step II specifically comprises:

[0040] Five drilling parameter correction items are selected: drill pressure correction item, torque correction item, rotation speed correction item, clean water pump station pressure correction item, and pump volume correction item. Based on the different engineering levels classified in step I, the correction value of each drilling parameter correction item is determined for each level.

[0041] Compared with existing technologies, this invention first uses geophysical exploration to obtain the porosity and water cut distribution of unexposed areas. Then, combining historical borehole data, a three-dimensional physical property matrix of the unexposed areas is constructed, and a three-dimensional continuous spatial distribution model is generated using Kriging interpolation. Next, based on the entropy weight-ideal point method, engineering levels are classified according to the porosity and water cut at different locations, and the correction values ​​for each drilling parameter correction item under each level are determined. The three-dimensional continuous spatial distribution model is combined with the engineering level classification to determine the engineering level corresponding to different locations in the model. When the drilling rig reaches a certain position, the corresponding drilling parameter correction item is called according to the engineering level of that position to correct the current drilling parameters, allowing the drilling rig to continue drilling according to the corrected parameters. Furthermore, for areas with abnormal risks, the borehole azimuth is adjusted in real time using measurement-while-drilling technology, and the drilling parameters are corrected according to the engineering level of different locations the drilling passes through, thereby ensuring accurate arrival at the target area. This invention, by integrating multi-source geophysical data to construct a porosity-water cut dual-factor collaborative driving model, achieves real-time correction of drilling parameters, promoting the upgrade of intelligent mining from passive response to active control. Attached Figure Description

[0042] Figure 1 This is an overall flowchart of the present invention;

[0043] Figure 2 This is a schematic diagram of the specific drilling process of the present invention;

[0044] Figure 3 This is a schematic diagram illustrating the effect of engineering grade classification in this invention. Detailed Implementation

[0045] The present invention will be further described below.

[0046] like Figure 1 As shown, the present invention includes the following steps:

[0047] Step 1: Multi-source geophysical data acquisition and 3D physical property modeling:

[0048] ① First, obtain seismic wave data and electrical resistivity data of the unexposed area in front of the tunnel face through geophysical exploration, and then obtain the porosity distribution and water content distribution of the unexposed area based on the seismic wave data and electrical resistivity data respectively;

[0049] ② Combining historical borehole data, a three-dimensional physical property matrix of the unexposed area ahead of the drilling face is constructed, specifically as follows:

[0050]

[0051] ③ Combine the porosity distribution and water content distribution from step ① with the three-dimensional physical property matrix from step ② to generate a three-dimensional continuous spatial distribution model, and mark the risk and anomaly areas;

[0052] Step 2: Porosity-water cut classification and determination of drilling parameter correction items:

[0053] I. Based on the Entropy Weighted Ideal Point Method (EW-TOPSIS), porosity and water content are classified into engineering grades. Porosity and water content weights are dynamically generated through information entropy, thereby classifying different locations in unexposed areas into different engineering grades. Specifically:

[0054] Entropy weighting method: quantifies the dispersion of data through information entropy, and assigns high weight to parameters with high dispersion.

[0055] Ideal point method: Calculate the closeness C between the parameter value and the optimal (safe) / worst (risk) solution to achieve continuous hierarchical classification;

[0056]

[0057] Specific steps:

[0058] (1) Data standardization and normalization:

[0059] Porosity normalization:

[0060] Moisture content normalization:

[0061]

[0062] (2) Calculate information entropy and entropy weight;

[0063]

[0064] (3) Define ideal solution: positive and negative ideal solutions correspond to the optimal safe working condition and the worst risk working condition, respectively;

[0065]

[0066] (4) Calculate the application progress C.

[0067] (5) Single-factor classification; Classification is performed using the progress tracking method, as shown in the table below;

[0068]

[0069] (6) Engineering grade classification: The dual-factor grade classification is coupled, compressed, and eliminates contradictory combinations and merges equivalent risk combinations;

[0070] The risk gradient within the engineering level is refined using a weighted formula R; the initial coefficients are obtained through empirical formulas and change continuously as the project progresses.

[0071] R = α·φ norm +β·S w,norm +γ·(φ·S w )

[0072] Where α = 0.6 represents the weight of the normalized porosity value; β = 0.4 represents the weight of the normalized moisture content value;

[0073] γ = 0.3, representing the coupling effect penalty coefficient; φ norm , representing normalized porosity (decimal); S w,norm φ·S represents the normalized moisture content (decimal). w This represents the product of the original porosity and the water content (%). 2 Finally, based on the R value, the project level was divided into seven levels, from G1 to G7. Figure 3 As shown;

[0074] Engineering level Combination logo Risk range Engineering features G1 P1W1 R≤0.2 Dense stable layer G2 P1W2 0.2<R≤0.4 Microfracture development layer G3 P2W1 / P1W2 0.3<R≤0.5 transition layer G4 P2W2 0.4<R≤0.6 Medium-hole-medium water layer G5 P2W3 0.5<R≤0.7 Seepage risk layer G6 P3W2 0.6<R≤0.8 Fault zone hidden danger layer G7 P3W3 R＞0.8 High-risk layer of sudden water inrush .

[0075] II. Select multiple drilling parameter correction items, and determine the correction value of each drilling parameter correction item at each level according to the different project levels divided in Step I, specifically as follows:

[0076] Select five drilling parameter correction items: drill pressure correction item, torque correction item, rotation speed correction item, clean water pump station pump pressure correction item, and pump volume correction item. Based on the different engineering levels divided in step I, determine the correction value of each drilling parameter correction item at each level.

[0077] Engineering level Drill pressure correction Torque correction Speed ​​correction Pump pressure correction Pump volume correction G1 +18％ +10％ +12％ -15％ +20％ G2 +10％ +5％ +5％ -8％ +15％ G3 ±0％ ±0％ ±0％ +10％ +10％ G4 -8％ -5％ -5％ +20％ -5％ G5 -15％ -12％ -12％ +30％ -15％ G6 -22％ -18％ -18％ +40％ -25％ G7 -30％ -25％ -25％ +60％ -30％ .

[0078] Step 3: Drilling Rig Parameter Coordination Correction Strategy

[0079] A. Combining the three-dimensional continuous spatial distribution model from Step 1 with the engineering grade classification from Step 2, determine the engineering grade corresponding to different locations on the model. When the drilling rig reaches a certain location, adjust the current drilling parameters according to the engineering grade of that location by calling the corresponding drilling parameter correction item from Step 2. Figure 2 As shown; in order to further ensure the drilling effect, the drilling pressure can be strengthened and corrected according to the water content at different locations;

[0080]

[0081] Among them, △P 实际 To enhance the corrected drilling pressure, ΔP represents the initial drilling pressure, and S... w S represents the moisture content at the current location. w,max Maximum moisture content;

[0082] B. Transmit the corrected drilling parameters to the drilling rig so that it can continue drilling at the current position according to the corrected drilling parameters;

[0083] Step 4: Directional drilling exploration of abnormal areas:

[0084] (i) Based on the risk anomaly areas marked in step one, plan the directional drilling trajectory (such as azimuth and dip angle);

[0085] (ii) The borehole orientation is adjusted in real time using measurement while drilling (MWD) technology, and the drilling parameters are corrected by repeating step three according to the engineering level of different locations the drilling passes through, so as to ensure accurate arrival at the target area;

[0086] (iii) During the drilling process, the three-dimensional continuous spatial distribution model generated in step one is dynamically verified based on the measured geological data (such as the amount of cuttings returned and the change in torque). If the requirements are not met, step one is repeated to regenerate the three-dimensional continuous spatial distribution model.

[0087] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing advanced drilling parameters based on a dual factor of porosity and water cut, characterized in that, Includes the following steps: Step 1: Multi-source geophysical data acquisition and 3D physical property modeling: ① First, obtain seismic wave data and electrical resistivity data of the unexposed area in front of the tunnel face through geophysical exploration, and then obtain the porosity distribution and water content distribution of the unexposed area based on the seismic wave data and electrical resistivity data respectively; ② Construct a three-dimensional physical property matrix of the unexposed area in front of the drilling face by combining historical drilling data; ③ Combine the porosity distribution and water content distribution from step ① with the three-dimensional physical property matrix from step ② to generate a three-dimensional continuous spatial distribution model, and mark the risk and anomaly areas; Step 2: Porosity-water cut classification and determination of drilling parameter correction items: I. Based on the entropy weight-ideal point method, porosity and water content are classified into engineering grades. Porosity and water content weights are dynamically generated through information entropy, and then different locations in the unexposed area are classified into different engineering grades. II. Select multiple drilling parameter correction items, and determine the correction value of each drilling parameter correction item at each level according to the different engineering levels divided in step I. Step 3: Drilling Rig Parameter Coordination Correction Strategy A. Combine the three-dimensional continuous spatial distribution model from step one with the engineering grade classification from step two to determine the engineering grade corresponding to different positions in the model. When the drilling rig drills to a certain position, the drilling parameter correction item corresponding to step two is called to correct the current drilling parameters according to the engineering grade of that position. B. Transmit the corrected drilling parameters to the drilling rig so that it can continue drilling at the current position according to the corrected drilling parameters; Step 4: Directional drilling exploration of abnormal areas: (i) Based on the risk and abnormal areas marked in step one, plan the directional drilling trajectory; (ii) The borehole orientation is adjusted in real time using measurement while drilling technology, and the drilling parameters are corrected by repeating step three according to the engineering level of different locations the drilling passes through, so as to ensure accurate arrival at the target area. (iii) During the drilling process, the three-dimensional continuous spatial distribution model generated in step one is dynamically verified based on the measured geological data. If it does not meet the requirements, step one is repeated to regenerate the three-dimensional continuous spatial distribution model.

2. The method for optimizing advanced drilling parameters based on a dual factor of porosity and water cut as described in claim 1, characterized in that, Step I specifically involves: Entropy weighting method: quantifies the dispersion of data through information entropy, and assigns high weight to parameters with high dispersion. Ideal point method: Calculate the closeness C between parameter values ​​and the optimal / worst solutions to achieve continuous hierarchical classification; Specific steps: (1) Data standardization and normalization: Porosity normalization: Moisture content normalization: f min =min(φ),φ max =max(φ) S w,min =min(S w ),S w,max =max(S w ) (2) Calculate information entropy and entropy weight; (3) Define ideal solution: positive and negative ideal solutions correspond to the optimal safe working condition and the worst risk working condition, respectively; (4) Calculate the application progress C. (5) Single-factor classification; using progress tracking for classification; (6) Engineering grade classification: The dual-factor grade classification is coupled, compressed, and eliminates contradictory combinations and merges equivalent risk combinations; The risk gradient within the engineering level is refined using a weighted formula R; the initial coefficients are obtained through empirical formulas and change continuously as the project progresses. R=a·φ norm +β·S w,norm +γ·(φ·S w ) Where α = 0.6, representing the weight of the normalized porosity value; β = 0.4, representing the weight of the normalized moisture content value; γ = 0.3, representing the coupling effect penalty coefficient; φ norm , representing normalized porosity; S w,norm φ·S represents the normalized moisture content. w It represents the product of the original porosity and the water content; finally, the engineering grade is divided into seven levels based on the R value.

3. The method for optimizing advanced drilling parameters based on a dual factor of porosity and water cut as described in claim 1, characterized in that, Step II specifically involves: Five drilling parameter correction items are selected: drill pressure correction item, torque correction item, rotation speed correction item, clean water pump station pressure correction item, and pump volume correction item. Based on the different engineering levels classified in step I, the correction value of each drilling parameter correction item is determined for each level.

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