Deep shale gas horizontal well geosteering method

Abstract

The invention discloses a deep shale gas horizontal well geosteering method, which belongs to the technical field of geosteering, and comprises the following steps: S1, multi-modal data acquisition, S2, quantum encryption and edge calculation, S3, dual-scale dynamic modeling, S4, cross-scale dynamic fusion, and S5, intelligent decision and error control. Through collaborative innovation of high-temperature-resistant multi-mode sensing, cross-scale dynamic modeling and intelligent decision making, the reliability, real-time performance and accuracy of ultra-deep shale gas horizontal well geosteering are remarkably improved, and comprehensive breakthrough of core indexes such as the reservoir penetration rate, the trajectory control accuracy and the development efficiency is achieved; key technical support is provided for efficient development of ultra-deep shale gas, the double-layer structure optical fiber probe design is adopted, high-precision monitoring of stratum strain is achieved through the inner-layer distributed Brillouin optical fiber, the outer-layer Raman optical fiber is combined with the sulfur-resistant alloy sheath and the high-temperature-resistant buffer layer, and the stability of the sensor in a severe environment is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of geosteering technology, and in particular to a geosteering method for deep shale gas horizontal wells. Background Art

[0002] As the core area of ​​unconventional oil and gas resource development in my country, the efficient exploitation of deep shale gas is of great strategic significance to ensuring energy security. my country's ultra-deep shale gas reservoirs (buried at a depth of 3,800-4,800 meters) generally present the characteristics of "three highs and two complexities": formation temperature reaches 170-210°C, pressure exceeds 150MPa, sulfur content is high (H2S partial pressure>30MPa), and the reservoir is highly heterogeneous (the thickness of a single layer "sweet spot" is only 2-6 meters), and microstructures are developed (high-angle faults, micron-level fracture networks). In such extreme environments, horizontal wells need to pass through complex formations and maintain high-precision trajectory within high-quality reservoirs (vertical error ≤ 0.5 meters, penetration rate ≥ 85%), which poses severe challenges to the high-temperature sensing, real-time fusion of multi-source data, complex formation identification and intelligent decision-making capabilities of geological guidance technology. Traditional geosteering methods have exposed multiple technical bottlenecks in ultra-deep applications: First, high-temperature, high-pressure sulfur corrosion leads to insufficient sensor reliability. Traditional resistive sensors have a measurement error exceeding 15%, and gamma logging data has a distortion rate of 40% due to pyrite interference, making it impossible to accurately capture formation parameters. Second, data transmission and processing lag, and conventional logging-while-drilling technology is affected by electromagnetic interference and has a bit error rate of >1×10 -6 , and the central processing unit (CPU) processing delay exceeds 90 seconds, making it difficult to meet the real-time requirements of ultra-deep rapid drilling (mechanical penetration rate > 4 meters per minute). Third, the geological model's multi-scale coordination capabilities are weak. Traditional static models rely solely on seismic and well logging data for macrostructural updates, resulting in an error of 1-2 meters in identifying fine structures such as microfractures and small faults (fault throw < 5 meters), resulting in trajectory adjustment lags exceeding 10 meters. Fourth, complex formation identification relies on manual experience, lacking effective data support for special scenarios such as sulfur-bearing formations and high-stress fracture zones. The lithology identification accuracy rate is only 70%-75%, and the reservoir penetration rate is generally below 70%. These issues have led to ultra-deep shale gas development facing difficulties such as low efficiency (long drilling cycle), high cost (instrument failure rate > 5 times / well), and poor safety (high risk of wellbore collapse). Technological innovation is needed to overcome the limitations of traditional methods. Summary of the Invention

[0003] (1) Technical problems solved In view of the shortcomings of the existing technology, the present invention provides a deep shale gas horizontal well geosteering method, which solves the problems existing in the above-mentioned background technology.

[0004] (2) Technical solution To achieve the above objectives, the present invention provides the following technical solution: a method for geosteering a deep shale gas horizontal well, comprising the following steps: S1. Multimodal Data Acquisition: During drilling in ultra-deep shale gas reservoirs at depths of 3,800-4,800 meters, temperatures of 170-210°C, and pressures of 80-150 MPa, a dual-layer fiber optic probe, MEMS accelerometer, and quantum magnetometer embedded in the drill collar sidewall collects real-time data on the strain field (accuracy ±20 με), temperature field (accuracy ±0.5°C), three components of the geomagnetic field, and formation dip and azimuth within a 15-meter radius of the wellbore. The inner layer of the dual-layer fiber optic probe is a distributed Brillouin fiber for monitoring formation strain, while the outer layer is a Raman fiber coated with a sulfur-resistant alloy for monitoring formation temperature. S2. Quantum encryption and edge computing: The multi-physics field data collected in step S1 are encrypted using a quantum key distribution module for polarization state encryption. The data is then transmitted to an edge computing node near the drill bit via wavelength division multiplexing (1550nm quantum channel and 1310nm data channel). A heterogeneous computing unit consisting of an FPGA chip and an AI chip is used to sequentially perform hardware filtering (50Hz power frequency notch), wavelet transform algorithm filtering (DB4 wavelet 5-layer decomposition), and CNN intelligent filtering. The raw data is subjected to three-layer noise reduction processing, achieving a data compression ratio of 150:1 and controlling data processing latency to less than 15 seconds. S3. Dual-scale dynamic modeling: Based on the dual-scale Kalman filter algorithm, multi-source data are integrated: at the long scale (10-100 meters), seismic data (resolution 0.3 meters) and logging-while-drilling data are integrated to update the macrostructural model of the formation; at the short scale (0.1-1 meter), optical fiber strain data (sampling interval 0.1 meters) are used to identify fine structures such as microcracks and lithologic interfaces through a morphological edge detection algorithm. The process noise covariance matrix uses the adaptive adjustment formula: ; (α=0.75, m=100, is the measured value, is the predicted value); S4. Cross-Scale Dynamic Fusion: A four-level cross-scale geological knowledge base was established, encompassing nanoscale mineral morphology (AFM data), micron-scale fracture networks (FIB-SEM data), meter-scale lithologic interfaces (well logging data), and kilometer-scale structures (seismic data). Graph neural networks were used to construct mapping relationships between data at different scales. For sulfur-bearing formations, historical data from ultra-deep shale gas fields worldwide was reused through transfer learning, combining reinforcement learning with a CNN+Transformer architecture for sulfur identification (with focal loss function weights γ=2 and α=0.75). S5. Intelligent decision-making and error control: Build a full-link error feedback mechanism: The sensor adopts triple-mode redundant design to reduce the probability of single point failure to 10 -6 times / hour; When the error between the measured value and the model prediction is greater than 0.5 meters, the model backtracking correction algorithm based on the genetic algorithm is activated to adjust parameters such as formation density and acoustic wave velocity so that the subsequent prediction error converges to within 0.2 meters; Adaptive trajectory adjustment is achieved through a dual-track decision-making system consisting of a rule engine (3,200+ ultra-deep guidance rules, response time <1 second) and a Transformer deep learning model (input 56-dimensional features, trajectory adjustment prediction error ≤0.3 meters), combined with 1,000 Monte Carlo working condition simulations to output a risk probability matrix.

[0005] Furthermore, the quantum encryption transmission module adopts the BB84 protocol combined with cascade error correction code, and maintains a key generation rate of ≥7.5Mbps and a bit error rate of <5×10-10 under a 200°C environment through Peltier cooling.

[0006] Furthermore, the FPGA chip of the edge computing node controls the junction temperature below 110°C through microchannel liquid cooling (the cooling medium is a fluorinated liquid with a boiling point of 190°C).

[0007] Furthermore, in the dual-scale Kalman filter algorithm, the long-scale model updates and integrates seismic data with LWD data (gamma, resistivity, and acoustic waves), while the short-scale model identifies lithologic interfaces by calculating the gradient of optical fiber strain data (curvature change rate > 0.05 / m).

[0008] Furthermore, the number of graph neural network nodes in the cross-scale geological knowledge base reaches 10 6 The edge weights are determined by Bayesian optimization.

[0009] Furthermore, the full-link error feedback system establishes an error transfer function:

[0010] (K1=0.4, K2=0.3, K3=0.3, corresponding to the weights of sensing error, transmission error, and modeling error, respectively).

[0011] Furthermore, the dual-track intelligent decision-making model's rule engine includes dedicated steering rules for ultra-deep shale gas reservoirs. When natural gamma is greater than 120 API and resistivity is greater than 80 Ω·m, it identifies the reservoir as high-quality and maintains the current trajectory. The deep learning model uses a Transformer architecture (12-layer encoder + 8-layer decoder) to output the optimal combination of drilling pressure, rotational speed, and displacement parameters. Furthermore, by building a drilling simulator based on a physics engine, we simulate typical working conditions such as crossing small faults (fault distance 1-5 meters), drilling into high-sulfur formations (sulfur content >1%), and passing through fracture zones (fracture density >5 fractures / meter), providing quantitative risk assessment for trajectory adjustment.

[0012] (3) Beneficial effects The present invention provides a method for geosteering deep shale gas horizontal wells, which has the following beneficial effects: Through the collaborative innovation of high-temperature multimodal sensing, cross-scale dynamic modeling, and intelligent decision-making, the reliability, real-time performance, and accuracy of geosteering for ultra-deep shale gas horizontal wells have been significantly improved. This has achieved comprehensive breakthroughs in core indicators such as reservoir penetration rate, trajectory control accuracy, and development efficiency, providing key technical support for the efficient development of ultra-deep shale gas. In response to the harsh conditions of ultra-deep, high temperature, high pressure and high sulfur, a double-layer fiber optic probe design is adopted. The inner layer of distributed Brillouin fiber realizes high-precision monitoring of formation strain. The outer layer of Raman fiber is combined with an anti-sulfur alloy sheath and a high-temperature resistant buffer layer, which significantly improves the stability of the sensor in harsh environments, effectively resists sulfur corrosion and thermal aging, ensures the acquisition accuracy of key parameters such as strain and temperature, provides a reliable data basis for geological guidance, and reduces guidance errors caused by sensor failure from the source.

[0013] By introducing quantum encryption transmission technology and edge intelligent processing modules, quantum key distribution enables highly secure data transmission with low error rates. Combined with heterogeneous computing units, this system performs multi-layer noise reduction and compression on raw data, significantly reducing data processing latency and meeting the real-time demands of ultra-deep, rapid drilling. This design effectively addresses the challenges of electromagnetic interference and processing lags in traditional data transmission, ensuring the guidance system's ability to respond promptly to formation changes and providing real-time data support for dynamic trajectory adjustments.

[0014] By integrating seismic data, logging-while-drilling data, and fiber-optic strain data based on a dual-scale Kalman filter algorithm, we achieve simultaneous updating and collaborative modeling of macrostructural models and microstructural fine structures. Long-scale data ensures accurate understanding of macroscopic trends in formations, while short-scale data captures subtle changes in microcracks and lithologic interfaces, effectively improving the ability to identify complex structures such as small faults and lithologic abrupt zones. This addresses the issue of traditional models lagging behind in reflecting subtle changes in ultra-deep formations, providing model support that is more aligned with actual geological conditions for precise trajectory control.

[0015] By building a full-link error feedback mechanism and a dual-track intelligent decision-making system, reducing the risk of single-point failures through sensor redundancy, and dynamically optimizing parameters with a model backtracking correction algorithm, the adaptability and accuracy of the geological model are improved. The collaborative work of the rule engine and deep learning model enables rapid decision-making in routine working conditions and precise analysis of complex working conditions. Simultaneously, quantitative risk assessment is provided through working condition simulation, significantly improving the scientific and safe nature of trajectory adjustments, reducing decision-making biases caused by reliance on human experience, effectively increasing the penetration rate of horizontal wells in high-quality reservoirs, and reducing the risk of complex accidents during the drilling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the hydraulic fracturing process of a deep shale gas horizontal well geosteering method of the present invention.

[0017] Figure 2 Schematic diagram of the intelligent control steps of a deep shale gas horizontal well geosteering method of the present invention Figure 3 This is a flow chart of step 2 of a deep shale gas horizontal well geosteering method according to the present invention. Figure 4 This is a flow chart of step three of a deep shale gas horizontal well geosteering method according to the present invention. Figure 5 This is a flow chart of step four of a deep shale gas horizontal well geosteering method according to the present invention. Figure 6 This is a flow chart of step five of a deep shale gas horizontal well geosteering method according to the present invention. DETAILED DESCRIPTION

[0018] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Example 1 like Figure 1-Figure 2 As shown, according to one aspect of the present invention, a technical solution is provided: a deep shale gas horizontal well geosteering method: 1. Implementation Scenario The project involved an ultra-deep shale gas well at a depth of 4,200 meters, with a reservoir temperature of 185°C, a formation pressure of 120 MPa, and an H2S partial pressure of 25 MPa. The target horizontal section was 3,500 meters long, requiring the project to penetrate a 4.2-meter-thick, organic-rich shale "sweet spot" reservoir. The geological characteristics of the area are as follows: The pyrite content is 15%, and there are three small faults with a fault distance of 1-3 meters; There are high-angle crack zones with a crack density of 6 cracks / m; The reservoir is highly heterogeneous, with a single "sweet spot" only 2-6 meters thick.

[0020] 2. Specific implementation steps S1: High-temperature multimodal sensor network deployment and data collection 1. Sensor hardware implementation Three sets of double-layer fiber optic probes with 120° intervals were embedded in the sidewall of a Φ215.9mm drill collar. The specific parameters are as follows: Inner layer: Distributed Brillouin fiber (germanium-doped quartz core, 8.3μm diameter), real-time monitoring of the strain field within 15 meters around the well, sampling rate 20kHz, accuracy ±20με; Outer layer: Raman fiber wrapped with Ni-Cr-Mo anti-sulfur alloy coating (Cr content 22%, Mo content 13%), monitors temperature field with an accuracy of ±0.5°C, and can work stably for more than 150 hours at 185°C and 120MPa. It also integrates a cesium atomic quantum magnetometer (resolution 0.1pT) and a MEMS accelerometer (accuracy 10 -6 g) Real-time calculation of formation azimuth (accuracy ±0.1°) and dip (accuracy ±0.2°).

[0021] 2. Data collection effect During a single drilling trip, 1.2×10 6 Group, temperature data 6×10 5 The group successfully identified two temperature anomaly areas (indicating the development of microcracks), with a data distortion rate of less than 5%, a 70% improvement over traditional sensors.

[0022] S2: Quantum Encryption Transmission and Edge Intelligent Data Preprocessing 1. Transmission and processing hardware The QKD module is deployed at the bottom of the well, using wavelength division multiplexing of a 1550nm quantum channel and a 1310nm data channel. Peltier cooling is used to maintain a key generation rate of 8Mbps at 200°C, with a bit error rate of 3×10 -10 , data transmission accuracy is 99.995%.

[0023] The edge computing node integrates a Xilinx Kintex UltraScale+ FPGA (microchannel liquid cooling controlled to 110°C) and a Cambricon MLU270 AI chip (computing power of 128TOPS), and performs three-level processing on raw data: Hardware filtering: 50Hz power frequency notch filters out drilling pump vibration noise; Wavelet transform: db4 wavelet 5-layer decomposition removes high-frequency random noise, improving the signal-to-noise ratio from 15dB to 30dB; CNN intelligent filtering: The trained ResNet-18 model identifies abnormal data points with an error rate of less than 2%.

[0024] 2. Processing efficiency The data compression ratio of a single well section (100 meters) reaches 160:1, with a processing delay of 12 seconds, meeting the real-time requirements of a mechanical drilling speed of 5 meters per minute.

[0025] S3: Construction and update of dual-scale dynamic geological model 1. Algorithm implementation Long-scale modeling: By integrating seismic data (prestack depth migration processing, 0.3-meter resolution) with logging-while-drilling data (gamma, resistivity, and acoustic), and updating the macrostructural model through dual-scale Kalman filtering, a small fault with a throw of 3 meters was identified, with a predicted position and an error of 0.8 meters between the actual drilling and the fault.

[0026] Short-scale identification: Gradient calculation of optical fiber strain data (curvature change rate > 0.05 / m) combined with a morphological edge detection algorithm can locate lithologic interfaces with an accuracy of 0.15 meters, successfully identifying the top and bottom interfaces of the reservoir, which is three times more accurate than traditional methods.

[0027] 2. Model update formula The process noise covariance matrix uses the adaptive adjustment formula: The model can be updated in seconds, and the accuracy of small fault identification reaches 92%.

[0028] S4: Stratigraphic identification driven by a cross-scale geological knowledge base 1. Knowledge base construction Establish a cross-scale geological knowledge base with a four-level data structure: Nanoscale: AFM scans the mineral surface to obtain the distribution of pyrite particles (particle size 5-20 μm); Micron level: FIB-SEM reconstructs the microcrack network and identifies three types of cracks (tensile, shear, and suture lines); Meter-level: LWD data was used to divide lithologic sections and identify five layers of sulfur-bearing shales; Kilometer-level: Seismic data was used to construct a structural model to determine the reservoir dip range of 2°-5°. A graph neural network (1.2×10 nodes) was used. 6 , edge weights are determined by Bayesian optimization) to establish a cross-scale mapping, and the sulfur mine identification sub-network (CNN+Transformer architecture) adopts a focal loss function (γ=2, α=0.75), and the accuracy of sulfur-bearing strata identification reaches 96%.

[0029] S5: Full-link error feedback and dual-track decision adjustment 1. Error control Sensor triple-mode redundancy: 3 sets of fiber optic probes independently collect data, and through majority voting algorithm, the probability of single point failure is reduced to 5×10 -7 times / hour, and no sensor failure occurred during the drilling process.

[0030] Model backtracking correction: When the error between the actual drilling trajectory and the model prediction reaches 0.6 meters, the genetic algorithm is activated to correct the formation density parameter (from 2.6g / cm 3 Adjusted to 2.75g / cm 3 ), and the subsequent prediction error converged to 0.18 meters.

[0031] 2. Intelligent Decision-Making Rule Engine: Executes 3,200 rules, including "Keep track when natural gamma is greater than 120 API and resistivity is greater than 80 Ω·m," with a response time of 0.8 seconds.

[0032] Deep learning model: Using the Transformer architecture (12-layer encoder + 8-layer decoder), the model inputs 56-dimensional feature parameters and outputs the optimal drilling weight (18-22 tons) and rotation speed (60-80 rpm). It successfully avoided two high-sulfur risk areas and achieved a trajectory adjustment error of 0.25 meters.

[0033] Monte Carlo simulation: The small fault crossing condition was simulated 1000 times, and the output of the wellbore collapse probability was 0.12, which guided the adjustment of the drilling fluid density to 1.45g / cm 3 , no collapse accidents occurred.

[0034] 3. Comparison of Implementation Effects

[0035] IV. Summary 1. Core technology implementation and innovative breakthroughs High temperature resistant multimodal sensing network: By integrating a double-layer fiber optic probe (the inner layer is Brillouin fiber for strain measurement, and the outer layer is Raman fiber for temperature measurement) with a quantum magnetometer, high-precision data acquisition (strain accuracy of ±20με, temperature accuracy of ±0.5℃) is achieved in a sulfur-containing environment of 185℃ and 120MPa. The data distortion rate is less than 5%, solving the problems of high-temperature failure and sulfur corrosion of traditional sensors.

[0036] Quantum encryption transmission and edge intelligent processing The use of quantum key distribution technology and heterogeneous computing units achieves a data compression ratio of 160:1 and a processing delay of 12 seconds, meeting the real-time requirements of ultra-deep rapid drilling, with a transmission bit error rate as low as 3×10 -10 , which is two orders of magnitude higher than traditional methods.

[0037] Construction of a dual-scale dynamic geological model The long-scale fusion of seismic and logging data updates the macrostructure, and the short-scale fiber optic strain data is used to identify microcracks (accuracy 0.15 meters). The accuracy of identifying small faults (fault distance ≥ 1 meter) reaches 92%, solving the problem of traditional models lagging behind in identifying subtle structures.

[0038] Cross-scale geological knowledge base and intelligent decision-making A four-level cross-scale knowledge base (nanometer to kilometer) was established. Through graph neural networks and reinforcement learning, the accuracy of sulfur-containing formation identification was increased to 96%. The dual-track decision-making system (rule engine + Transformer model) achieved a trajectory adjustment error of ≤0.3 meters and a risk assessment quantification rate of 100%.

[0039] 2. The project implementation effect has been significantly improved

[0040] 3. Scenario Universality and Industry Value Applicability: The implementation scenarios cover ultra-deep, high-temperature, high-pressure, sulfur-containing and microstructurally developed strata. The technical solution can be directly extended to similar blocks such as Fuling and Weiyuan, as well as the development of unconventional oil and gas reservoirs such as tight sandstone gas and coalbed methane.

[0041] Technical significance: A full-link intelligent system of "sensing-transmission-modeling-decision-making" has been built, breaking through the multiple bottlenecks of traditional methods in extreme environments and providing a replicable technical paradigm for the efficient development of ultra-deep shale gas.

[0042] Economic and safety value: The cost of single-well development was reduced by 28.6%, and the rate of complex accidents such as wellbore collapse decreased by 76.3%, achieving a dual improvement in economic benefits and engineering safety.

[0043] 5. Summary Specific engineering validation has demonstrated the advancement and reliability of this invention in geosteering ultra-deep shale gas horizontal wells. The collaborative innovation of core technologies (high-temperature sensing, cross-scale modeling, and intelligent decision-making) effectively addresses the technical pain points of existing methods. Its quantitative effectiveness and universal applicability lay a solid foundation for the industrial application of this technology, which is of great significance for promoting the efficient development of unconventional oil and gas resources.

[0044] By deploying a double-layer optical fiber probe (anti-sulfur alloy sheath, high-temperature resistant polyimide buffer layer), quantum encryption transmission module (bit error rate < 5×10 -10), edge computing nodes (15-second data processing delay), dual-scale Kalman filter algorithm (model update in seconds) and cross-scale geological knowledge base (96% accuracy in identifying sulfur-containing formations), to build a full-link error feedback and dual-track decision-making system, achieving a reservoir penetration rate of 91.2% (an increase of 40.3% over traditional methods), a vertical trajectory error of 0.32 meters (a decrease of 73.3%), a drilling cycle of 29 days (shortened by 35.6%), and a logging instrument failure rate of 0.6 times / well (a decrease of 87.5%). It effectively solves the problems of sensor failure, data lag, and insufficient model accuracy in ultra-deep, high-temperature, and high-pressure sulfur corrosion environments, and provides a replicable intelligent geosteering technology paradigm for the efficient development of unconventional oil and gas reservoirs.

[0045] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0046] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A geosteering method for deep shale gas horizontal wells, characterized in that: The following steps are involved: S1. Multimodal Data Acquisition: During drilling in ultra-deep shale gas reservoirs at depths of 3,800-4,800 meters, temperatures of 170-210°C, and pressures of 80-150 MPa, a dual-layer fiber optic probe, MEMS accelerometer, and quantum magnetometer embedded in the drill collar sidewall collects real-time data on the strain field (accuracy ±20 με), temperature field (accuracy ±0.5°C), three components of the geomagnetic field, and formation dip and azimuth within a 15-meter radius of the wellbore. The inner layer of the dual-layer fiber optic probe is a distributed Brillouin fiber for monitoring formation strain, while the outer layer is a Raman fiber coated with a sulfur-resistant alloy for monitoring formation temperature. S2. Quantum encryption and edge computing: The multi-physics field data collected in step S1 are encrypted using a quantum key distribution module for polarization state encryption. The data is then transmitted to an edge computing node near the drill bit via wavelength division multiplexing (1550nm quantum channel and 1310nm data channel). A heterogeneous computing unit consisting of an FPGA chip and an AI chip is used to sequentially perform hardware filtering (50Hz power frequency notch), wavelet transform algorithm filtering (DB4 wavelet 5-layer decomposition), and CNN intelligent filtering. The raw data is subjected to three-layer noise reduction processing, achieving a data compression ratio of 150:1 and controlling data processing latency to less than 15 seconds. S3. Dual-scale dynamic modeling: Based on the dual-scale Kalman filter algorithm, multi-source data is integrated: long-scale (10-100 meters) seismic data (resolution 0.3 meters) and logging while drilling data are integrated to update the macro-structural model of the formation; At the short scale (0.1-1 m), optical fiber strain data (sampling interval 0.1 m) is used to identify fine structures such as microcracks and lithologic interfaces through a morphological edge detection algorithm. The process noise covariance matrix uses an adaptive adjustment formula: ,(α=0.75,m=100, is the measured value, is the predicted value); S4. Cross-Scale Dynamic Fusion: A four-level cross-scale geological knowledge base was established, encompassing nanoscale mineral morphology (AFM data), micron-scale fracture networks (FIB-SEM data), meter-scale lithologic interfaces (well logging data), and kilometer-scale structures (seismic data). Graph neural networks were used to construct mapping relationships between data at different scales. For sulfur-bearing formations, historical data from ultra-deep shale gas fields worldwide was reused through transfer learning, combining reinforcement learning with a CNN+Transformer architecture for sulfur identification (with focal loss function weights γ=2 and α=0.75). S5. Intelligent decision-making and error control: Build a full-link error feedback mechanism: The sensor adopts triple-mode redundant design to reduce the probability of single point failure to 10 -6 times / hour; When the error between the measured value and the model prediction is greater than 0.5 meters, the model backtracking correction algorithm based on the genetic algorithm is activated to adjust parameters such as formation density and acoustic wave velocity so that the subsequent prediction error converges to within 0.2 meters; Adaptive trajectory adjustment is achieved through a dual-track decision-making system consisting of a rule engine (3,200+ ultra-deep guidance rules, response time <1 second) and a Transformer deep learning model (input 56-dimensional features, trajectory adjustment prediction error ≤0.3 meters), combined with 1,000 Monte Carlo working condition simulations to output a risk probability matrix.

2. The deep shale gas horizontal well geosteering method according to claim 1, characterized in that: The core of the double-layer optical fiber probe is germanium-doped quartz optical fiber, the cladding is pure quartz, the buffer layer is polyimide with a temperature resistance of 250°C, and the sheath is a Ni-Cr-Mo sulfur-resistant alloy coating (Cr content 22%, Mo content 13%).

3. The deep shale gas horizontal well geosteering method according to claim 1, characterized in that: The quantum encryption transmission module adopts the BB84 protocol combined with cascade error correction code, and maintains a key generation rate of ≥7.5Mbps and a bit error rate of <5×10-10 under a 200°C environment through Peltier cooling.

4. The method for geosteering a deep shale gas horizontal well according to claim 1, characterized in that: The FPGA chip of the edge computing node controls the junction temperature below 110°C through microchannel liquid cooling (the cooling medium is a fluorinated liquid with a boiling point of 190°C).

5. The deep shale gas horizontal well geosteering method according to claim 1, characterized in that: In the dual-scale Kalman filter algorithm, the long-scale model updates and fuses seismic data with logging-while-drilling data (gamma, resistivity, and acoustic waves), while the short-scale model identifies lithologic interfaces by calculating the gradient of optical fiber strain data (curvature change rate > 0.05 / m).

6. The deep shale gas horizontal well geosteering method according to claim 1, characterized in that: The number of graph neural network nodes in the cross-scale geological knowledge base reaches 10 6 The edge weights are determined by Bayesian optimization.

7. The deep shale gas horizontal well geosteering method according to claim 1, characterized in that: The full-link error feedback system establishes an error transfer function: , (K1=0.4, K2=0.3, K3=0.3, corresponding to the weights of sensing error, transmission error, and modeling error, respectively).

8. The deep shale gas horizontal well geosteering method according to claim 1, characterized in that: The rule engine of the dual-track intelligent decision-making model includes dedicated steering rules for ultra-deep shale gas reservoirs. When natural gamma is greater than 120 API and resistivity is greater than 80 Ω·m, it is determined to be a high-quality reservoir and the current trajectory is maintained. The deep learning model uses a Transformer architecture (12-layer encoder + 8-layer decoder) to output the optimal combination of drilling pressure, rotational speed, and displacement parameters.

9. The deep shale gas horizontal well geosteering method according to claim 1, characterized in that: By building a drilling simulator based on a physics engine, we simulate typical working conditions such as crossing small faults (fault distance 1-5 meters), drilling into high-sulfur formations (sulfur content > 1%), and passing through fracture zones (fracture density > 5 fractures / meter), providing quantitative risk assessment for trajectory adjustment.

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