Advanced exploration drilling parameter optimization method based on porosity-moisture content double factors

Abstract

The invention discloses an advanced exploration drilling parameter optimization method based on porosity-moisture content double factors, which comprises the following steps: acquiring porosity distribution and moisture content distribution of an unexposed area by geophysical prospecting, and constructing a three-dimensional physical property matrix of the unexposed area in combination with historical drilling data so as to generate a three-dimensional continuous space distribution model; then performing engineering grade division according to the porosity and the water content of different positions based on an entropy weight-ideal point method, and determining a correction value of each drilling parameter correction item under each grade; the three-dimensional continuous space distribution model is combined with engineering grade division, when a certain position is drilled, a corresponding drilling parameter correction item is called according to the engineering grade of the position to correct a current drilling parameter, and then drilling continues according to the corrected parameter; according to the method, the porosity-water content two-factor collaborative driving model is constructed by fusing multi-source geophysical prospecting data, real-time correction of drilling parameters is achieved, and upgrading of intelligent mining of mines from passive response to active regulation is promoted.

Description

Technical Field

[0001] The invention belongs to the technical field of mine borehole detection, and in particular relates to an advanced drilling parameter optimization method based on a porosity-water content dual factor. Background Art

[0002] In underground advanced directional drilling projects, precise control of drilling parameters is a core technology for achieving efficient water exploration and drainage, while ensuring safe construction. As mining depth increases, the dynamic coupling between porosity and water content in the strata becomes increasingly pronounced. This is especially true in 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, rotational speed, and mud parameters, making it difficult to respond to changes in stratum physical properties in real time. This results in low drilling efficiency in hard rock formations, frequent borehole wall instability in water-rich sections, and even water inrush accidents, seriously threatening mine safety and production.

[0003] While current geophysical exploration techniques (such as seismic and electrical methods) can proactively acquire multi-source formation data, existing drilling parameter control methods face the following challenges: First, the interpretation accuracy of geophysical data is insufficient, making it difficult for a single physical property parameter to characterize the synergistic mechanism between porosity and water content. Second, parameter correction lags, and the lack of closed-loop control logic based on dynamic physical property grading limits the adaptive capabilities of the drilling system. For example, in areas with concealed water channels, traditional methods are unable to optimize grouting processes based on real-time water content changes, easily leading to plugging failures. Furthermore, in emergency rescue scenarios, drilling trajectory adjustments rely on manual experience, making it difficult to quickly establish life-saving channels.

[0004] Therefore, how to provide a new drilling parameter optimization method, construct a porosity-water content dual-factor collaborative driving model by fusing multi-source geophysical data, and realize real-time correction of drilling parameters to solve the technical problem of balancing drilling efficiency and safety in complex formations, is the research direction required by the present invention. Summary of the Invention

[0005] In response to the problems existing in the above-mentioned prior art, the present invention provides a method for optimizing advanced exploration drilling parameters based on the porosity-water content dual factors. By integrating multi-source geophysical data, a porosity-water content dual-factor collaborative driving model is constructed to achieve real-time correction of drilling parameters, thereby promoting the upgrade of intelligent mining in mines from passive response to active regulation.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a method for optimizing advanced drilling parameters based on the dual factors of porosity and water content, 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 data of the unexposed area in front of the tunnel face through geophysical exploration, and obtain the porosity distribution and water content distribution of the unexposed area based on the seismic wave data and electrical data respectively;

[0009] ② Combine historical drilling data to construct a three-dimensional physical property matrix of the uncovered area in front of the tunnel face;

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

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

[0012] I. Engineering grade classification of porosity and water content based on the entropy weight-ideal point method (EW-TOPSIS). The porosity and water content weights are dynamically generated through information entropy, and different locations in the uncovered area are then divided 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 collaborative correction strategy:

[0015] A. Combine the three-dimensional continuous spatial distribution model from step 1 with the engineering grade classification from step 2 to determine the engineering grades corresponding to different locations in the model. When the drilling rig reaches a certain location, the drilling parameter correction item corresponding to step 2 is called according to the engineering grade of the location to correct the current drilling parameters.

[0016] B. Transmitting the corrected drilling parameters to the drilling rig so that it continues drilling at the current position according to the corrected drilling parameters;

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

[0018] (i) Plan the directional drilling trajectory (e.g., azimuth and inclination) based on the risk anomaly areas marked in step 1;

[0019] (ii) Use measurement while drilling (MWD) technology to adjust the drilling direction in real time. Repeat step 3 to modify the drilling parameters according to the engineering level of different locations to ensure accurate arrival at the target area.

[0020] ㈢ During the drilling process, the three-dimensional continuous spatial distribution model generated in step one is dynamically verified according to the measured geological data (such as the amount of rock cuttings returned and the torque change). If it does not meet the requirements, step one is repeated to regenerate the three-dimensional continuous spatial distribution model.

[0021] Furthermore, the step I is specifically as follows:

[0022] Entropy weight method: quantify data discreteness through information entropy, and assign high weight to high discreteness parameters;

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

[0024]

[0025] Specific steps:

[0026] (1) Data standardization and normalization:

[0027] Porosity normalization:

[0028] Moisture content normalized:

[0029]

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

[0031]

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

[0033] (4) Calculate the progress C,

[0034] (5) Single factor grading; grading using progress tracking;

[0035] (6) Engineering grade classification: dual-factor grade classification coupling compression eliminates contradictory combinations and merges equivalent risk combinations;

[0036] The risk gradient within the project level is refined through the weighted formula R; the initial coefficient is obtained through an empirical formula and changes continuously as the project progresses;

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

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

[0039] Furthermore, the step II is specifically as follows:

[0040] Select five drilling parameter correction items, namely, the bit pressure correction item, the torque correction item, the speed correction item, the clean water pump station pressure correction item, and the pump volume correction item. According to the different project levels divided in step I, determine the correction value of each drilling parameter correction item at each level.

[0041] Compared with the existing technology, the present invention first uses geophysical exploration to obtain the porosity and water content distribution of the uncovered area. Then, combined with historical drilling data, a three-dimensional physical property matrix of the uncovered area is constructed, and a three-dimensional continuous spatial distribution model is generated using Kriging interpolation. Then, based on the entropy weight-ideal point method, the engineering grade is divided according to the porosity and water content at different locations, and the correction value of each drilling parameter correction item under each grade is determined. The three-dimensional continuous spatial distribution model is combined with the engineering grade division to determine the engineering grade corresponding to different locations in the model. When the drill rig drills to a certain location, the corresponding drilling parameter correction item is called according to the engineering grade of the location to correct the current drilling parameters, so that the drill rig continues drilling according to the corrected parameters. In addition, for risk anomaly areas, the borehole orientation is adjusted in real time by using measurement while drilling technology, and the drilling parameters are corrected according to the engineering grade of different locations through which the drill passes, thereby ensuring accurate arrival at the target area. The present invention constructs a porosity-water content dual-factor collaborative driving model by fusing multi-source geophysical data, realizes real-time correction of drilling parameters, and promotes the upgrade of intelligent mining from passive response to active regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is the overall flow chart of the present invention;

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

[0044] Figure 3 It is a schematic diagram of the engineering grade classification effect in the present invention. DETAILED DESCRIPTION

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

[0049] ② Combined with historical drilling data, a three-dimensional physical property matrix of the uncovered area in front of the tunnel face was constructed, specifically:

[0050]

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

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

[0053] I. Engineering grade classification of porosity and water content is performed based on the entropy weight-ideal point method (EW-TOPSIS). Porosity and water content weights are dynamically generated through information entropy, and different locations in the uncovered area are then divided into different engineering grades. Specifically:

[0054] Entropy weight method: quantify data discreteness through information entropy, and assign high weight to high discreteness parameters;

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

[0056]

[0057] Specific steps:

[0058] (1) Data standardization and normalization:

[0059] Porosity normalization:

[0060] Moisture content normalized:

[0061]

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

[0063]

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

[0065]

[0066] (4) Calculate the progress C,

[0067] (5) Single factor classification: Classification is performed using the progress of the posting process, as shown in the table below;

[0068]

[0069] (6) Engineering grade classification: dual-factor grade classification coupling compression eliminates contradictory combinations and merges equivalent risk combinations;

[0070] The risk gradient within the project level is refined through the weighted formula R; the initial coefficient is obtained through an empirical formula and changes continuously as the project progresses;

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

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

[0073] γ=0.3, indicating the penalty coefficient of coupling effect; φ norm , represents the normalized porosity (decimal); S w,norm Indicates normalized moisture content (decimal); φ·S w It represents the product of original porosity and water content (% 2 ); Finally, the engineering grade is divided into seven grades from G1 to G7 according to the R value. Figure 3 As shown;

[0074] Engineering Grade Combination logo Risk range Engineering Features G1 P1W1 R≤0.2 Dense stable layer G2 P1W2 0.2<R≤0.4 Microcrack development layer G3 P2W1 / P1W2 0.3<R≤0.5 transition layer G4 P2W2 0.4<R≤0.6 Mesopore-middle water layer G5 P2W3 0.5<R≤0.7 Seepage risk layer G6 P3W2 0.6<R≤0.8 Hidden danger layer in the broken zone G7 P3W3 R＞0.8 High-risk layer for 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 engineering levels divided in step I. Specifically:

[0076] Select five drilling parameter correction items, namely, the bit pressure correction item, the torque correction item, the speed correction item, the clean water pump station pressure correction item, and the pump volume correction item, and determine the correction value of each drilling parameter correction item at each level according to the different project levels divided in step I;

[0077] Engineering Grade WOB 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 collaborative correction strategy:

[0079] A. Combine the three-dimensional continuous spatial distribution model in step 1 with the engineering grade classification in step 2 to determine the engineering grade corresponding to different locations in the model. When the drilling rig drills to a certain location, the drilling parameter correction item corresponding to step 2 is called according to the engineering grade of the location to correct the current drilling parameters. 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 is the drill pressure after strengthening correction, △P is the initial drill pressure, S w is the moisture content at the current location, S w,max is the maximum moisture content;

[0082] B. Transmitting the corrected drilling parameters to the drilling rig so that it continues drilling at the current position according to the corrected drilling parameters;

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

[0084] (i) Plan the directional drilling trajectory (e.g., azimuth and inclination) based on the risk anomaly areas marked in step 1;

[0085] (ii) Use measurement while drilling (MWD) technology to adjust the drilling direction in real time. Repeat step 3 to modify the drilling parameters according to the engineering level of different locations to ensure accurate arrival at the target area.

[0086] ㈢ During the drilling process, the three-dimensional continuous spatial distribution model generated in step one is dynamically verified according to the measured geological data (such as the amount of rock cuttings returned and the torque change). If it does not meet the requirements, step one is repeated to regenerate the three-dimensional continuous spatial distribution model.

[0087] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing advance drilling parameters based on the dual factors of porosity and water content, characterized in that: The following steps are involved: Step 1: Multi-source geophysical data acquisition and 3D physical property modeling: ① First, obtain seismic wave data and electrical data of the unexposed area in front of the tunnel face through geophysical exploration, and obtain the porosity distribution and water content distribution of the unexposed area based on the seismic wave data and electrical data respectively; ② Combine historical drilling data to construct a three-dimensional physical property matrix of the uncovered area in front of the tunnel face; ③ Combine the porosity distribution and water content distribution from step ① with the three-dimensional physical property matrix from step ② using Kriging interpolation to generate a three-dimensional continuous spatial distribution model, and mark the risk anomaly areas; Step 2: Porosity-water content classification and determination of drilling parameter correction items: I. Engineering grade classification of porosity and water content is performed based on the entropy weight-ideal point method. The porosity and water content weights are dynamically generated through information entropy, and different locations in the uncovered area are then divided 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 collaborative correction strategy: A. Combine the three-dimensional continuous spatial distribution model from step 1 with the engineering grade classification from step 2 to determine the engineering grades corresponding to different locations in the model. When the drilling rig reaches a certain location, the drilling parameter correction item corresponding to step 2 is called according to the engineering grade of the location to correct the current drilling parameters. B. Transmitting the corrected drilling parameters to the drilling rig so that it continues drilling at the current position according to the corrected drilling parameters; Step 4: Directional drilling exploration in abnormal areas: (1) Plan the directional drilling trajectory based on the risk anomaly areas marked in step 1; (ii) Use measurement while drilling technology to adjust the drilling direction in real time. Repeat step 3 to modify the drilling parameters according to the engineering level of different locations to ensure accurate arrival at the target area. ㈢ During the drilling process, the three-dimensional continuous spatial distribution model generated in step one is dynamically verified according to 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 the porosity-water content dual factor according to claim 1, characterized in that: The step I is specifically as follows: Entropy weight method: quantify data discreteness through information entropy, and assign high weight to high discreteness parameters; Ideal point method: calculate the closeness C between the parameter value and the optimal / worst solution to achieve continuous classification; Specific steps: (1) Data standardization and normalization: Porosity normalization: Moisture content normalized: 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 the ideal solution: positive and negative ideal solutions correspond to the optimal safety condition and the worst risk condition, respectively; (4) Calculate the progress C, (5) Single factor grading; grading using progress tracking; (6) Engineering grade classification: dual-factor grade classification coupling compression eliminates contradictory combinations and merges equivalent risk combinations; The risk gradient within the project level is refined through the weighted formula R; the initial coefficient is obtained through an empirical formula and changes continuously as the project progresses; R=a·φ norm +β·S w,norm +γ·(φ·S w ) Where α = 0.6, which represents the weight of the normalized porosity value; β = 0.4, which represents the weight of the normalized water content value; γ = 0.3, which represents the penalty coefficient of the coupling effect; φ norm , represents the normalized porosity; S w,norm represents the normalized moisture content; φ·S w It represents the product of original porosity and water content; finally, the engineering grade is divided into seven levels according to the R value.

3. The method for optimizing advanced drilling parameters based on the porosity-water content dual factor according to claim 1, characterized in that: The step II is specifically as follows: Select five drilling parameter correction items, namely, the bit pressure correction item, the torque correction item, the speed correction item, the clean water pump station pressure correction item, and the pump volume correction item. According to the different project levels divided in step I, determine the correction value of each drilling parameter correction item at each level.

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