5305 results about "Well drilling" patented technology

The invention provides an intelligent drilling speed prediction method based on physical feature guidance and multi-source information fusion, and relates to the technical field of intelligent drilling speed prediction, and the method specifically comprises the following steps: collecting multi-source heterogeneous data from a drilling real-time database, a logging system, a logging system and a geological database; constructing a dual-channel deep learning prediction model, wherein the dual-channel deep learning prediction model comprises a dual-channel convolution feature extraction module, a feature fusion module, a time sequence fusion module, a time sequence modeling module and a full connection layer which are connected in sequence; obtaining a predicted drilling speed by using a dual-channel deep learning prediction model; a joint loss function is constructed by considering a data driving error and a physical constraint error, an error is calculated according to the joint loss function, and network parameters are updated through back propagation; carrying out loop iteration training until convergence; and the trained dual-channel deep learning prediction model is used for drilling speed prediction. According to the technical scheme, the problems that in the prior art, a mechanism model is insufficient in precision, and a data driving model is poor in reliability are solved.

The invention relates to the technical field of geothermal drilling monitoring management, in particular to a construction monitoring system and method for geothermal drilling. The method comprises the following steps: constructing a state variable set, quantifying structural disturbance intensity by adopting a disturbance derivative method, normalizing the structural disturbance intensity, and defining a state transition tensor mapping behavior influence coefficient; in combination with the high-order derivative of the disturbance trend function and the behavior collaborative change matrix, an offset risk factor is generated through fusion for early warning; dividing a well drilling area into three-dimensional grids, calculating local stress potential energy, adjusting weight through a dynamic sensitive belt, and generating a risk distribution diagram through smooth filtering; performing closed-loop dynamic regulation and control by using risk potential energy centroid coordinates and gradient field inversion intervention vectors for medium and high risk areas; and through multi-period risk potential energy tensor analysis, calculating the matching degree of a self-healing index and a stable cone region, evaluating the self-healing capability of the system and feeding back a regulation and control strategy. According to the invention, a closed-loop monitoring and active control system for nonlinear risks of geothermal drilling is formed.

The invention relates to the technical field of drilling parameter control, in particular to an exploration drilling parameter control method and system.The method comprises the following steps that a drilling target and a target priority corresponding to the drilling target are set, and the target priority indicates the importance of the drilling target; acquiring various measurement data in the drilling process, and processing the various measurement data; according to the processed multiple measurement data, the current stratum characteristics and the current drilling tool operation state are recognized; according to geological information in the drilling process, the drilling target and the target priority are adjusted; according to real-time geological information and a drilling state, a drilling target and a parameter control strategy are dynamically adjusted, so that the method has the advantages of better adapting to complex stratum changes, optimizing tradeoff among multiple targets and improving drilling self-adaptability and control precision.

The invention relates to the technical field of petroleum engineering intelligent drilling data processing, in particular to a multi-source data fusion while-drilling formation pressure monitoring system and method, and the system comprises a data collection verification module, a data transmission alignment module, a pressure prediction engine module, a closed-loop optimization module and an intelligent regulation and control module. The data acquisition and verification module acquires pore pressure, annulus equivalent circulation density and vibration spectrum data through the circumferential sensor array, the closed-loop optimization module updates model parameters based on measured data after drilling and triggers periodic reconstruction, and the geological rule constraint unit verifies a gradient change threshold. The intelligent regulation and control module generates a slurry density adjustment instruction and rechecks and issues the slurry density adjustment instruction through the accident case knowledge base; and the three-dimensional dynamic rendering unit realizes pressure gradient visualization. The problems of prediction hysteresis, model mismatch and frequent manual intervention in formation pressure monitoring while drilling are solved through multi-source data real-time fusion, model dynamic optimization and autonomous decision closed loop.

The invention provides a multi-sensor dynamic calibration method in rotary geosteering drilling, and relates to the technical field of petroleum engineering and drilling, and the method comprises the following steps: collecting original sensing data in real time through a multi-source sensor array deployed on a rotary geosteering drilling tool, wavelet packet decomposition is combined with an improved sliding window algorithm to carry out online denoising processing on original data, and a three-dimensional feature matrix containing environmental interference factors is established; and based on the three-dimensional feature matrix, constructing a dynamic calibration model based on depth time sequence association, respectively processing sensor body feature flow and environment interference feature flow by using a dual-channel LSTM network, performing dynamic weight distribution and fusion on dual-channel features by means of a gating attention mechanism, and outputting a dynamic deviation compensation coefficient of each sensor. The parameter set is generated and reconstructed through the three-dimensional feature matrix, the two-channel network and cooperative calibration, the drilling efficiency and precision are improved, errors are reduced, and intelligent development is promoted.

The invention relates to the technical field of unconventional oil-gas exploration, and discloses a well-seismic integrated horizontal well geosteering risk evaluation method, which comprises the following steps: S1, acquiring three-dimensional seismic data, measurement while drilling data and geological logging data through a well-seismic integrated data acquisition platform; s2, constructing a horizontal well geosteering dynamic model based on multi-source heterogeneous data, and integrating a seismic inversion result and real-time drilling parameters; by establishing a well-seismic data dynamic fusion mechanism and a multi-source risk quantification model, the problem of space-time dislocation of seismic inversion and measurement-while-drilling data is solved, the recognition precision of a fault boundary, a lithologic interface and a pressure abnormal zone is ensured to be matched with drilling position changes in real time, risk misjudgment caused by model updating lag in a traditional method is eliminated, and the accuracy of the method is improved. The accuracy and timeliness of geosteering decision making of the complex structure area are improved; by monitoring well track deviation and geological model prediction deviation in real time, the risks of well wall instability and target deviation are avoided, and the operation safety of the horizontal well in the full life cycle is guaranteed.

The invention relates to an intelligent inversion method and system for well drilling overflow shaft gas distribution based on a self-encoder, and belongs to the technical field of oil gas and geothermal development drilling and completion engineering. Comprising the following steps: step 1, accurately solving multiphase flow parameters of a shaft; 2, in combination with the drilling working condition and the geological condition of a specific overflow high-risk well section, based on a Monte Carlo sampling method, eight parameters are changed, uniform sampling is carried out, and a high-precision simulation data set is formed; step 3, constructing a neural network model for gas cut state inversion based on an auto-encoder neural network; 4, training is carried out; 5, standardizing one-dimensional time sequence parameters in the monitoring data; and inputting into a trained neural network model for gas cut state inversion based on an auto-encoder neural network to obtain distribution data of the overflow gas in the shaft in a time period in which the time sequence parameter at the current moment is located. According to the invention, the real-time rapid inversion of the gas distribution in the parallel cylinder during the drilling overflow parallel control period is realized.

The invention provides a well drilling automatic control system which comprises an input establishing module used for inputting well track design parameters and establishing a well drilling system dynamic model; the target trajectory planning module is used for verifying stratum parameters according to the actual drilling data and planning the target trajectory of the current drilling section based on the verification result by using the drilling system dynamics model to obtain the target trajectory; the control quantity feed-forward calculation module is used for calculating the control quantity of the drilling machine by adopting a feed-forward algorithm based on the target track parameters, controlling the drilling real-time calibration module of the drilling machine, and correcting the control quantity in real time based on real-time evaluation of drilling errors in the drilling process; and the data output module is used for outputting logging data in the drilling process of the column after the preset drilling distance of the column is reached. Full-intelligent operation in the drilling process is achieved, and the precision, safety and oil and gas recovery efficiency of drilling operation are effectively improved.

The invention relates to a wellbore multiphase flow model numerical solution and gas-liquid distribution state inversion method and system, and belongs to the technical field of petroleum engineering, and the method comprises the steps: 1, constructing and training a physical information neural network for drilling wellbore multiphase flow dynamic simulation and overflow gas distribution state inversion; determining input and output of the physical information neural network; determining a loss function of the physical information neural network; training a physical information neural network; 2, designing an adaptive optimization algorithm, optimizing the final solution precision and convergence speed of the physical information neural network, and obtaining an adaptive physical information neural network; designing an adaptive activation function; designing a self-adaptive sampling mechanism based on residual errors; 3, based on the self-adaptive physical information neural network, numerical solution and gas-liquid distribution state inversion of the shaft multiphase flow model are achieved. According to the method, the problem that a traditional numerical method usually needs high-precision grid division and a large number of computing resources is effectively solved.

The invention provides a drilling risk determination method and device. Before specific implementation, a graph auto-encoder is introduced and used, and a preset detection model which is based on physical constraints and has a good application effect is obtained through unsupervised learning and training. In specific implementation, current target logging data of a target well and a logging data set of a current time period can be firstly obtained; determining a current working condition according to the target logging data; according to the current working condition, the logging data set of the current time period and a preset geological-engineering pre-drilling evaluation profile, a target dynamic threshold value which aims at a current target well and is based on working condition constraints and considers the change condition of data processed in the current time period before graph self-coding is determined; processing the target logging data by using a preset detection model to obtain a corresponding target reconstruction error; and detecting whether a drilling risk exists according to the target reconstruction error and the target dynamic threshold. Therefore, the drilling risk can be accurately detected and identified, and the false alarm rate is reduced.

The invention discloses a logging-while-drilling reservoir parameter dynamic prediction method based on double attention convolution LSTM, and the method comprises the steps: obtaining logging-while-drilling data and drilling data in real time, and carrying out the preprocessing of the data; establishing a DTW dynamic time window model based on a gradual transition strategy, and adaptively adjusting the length of an input sequence according to the stratum change speed; a double attention convolution LSTM model is combined with the DTW dynamic time window model to establish a feature-time double attention mechanism, and two dimensions of features and time are adaptively fused based on the convolution LSTM; setting a buffer area, storing data and controlling data quality; setting an online increment fine tuning strategy; and performing instance training, and performing multi-angle evaluation on the performance of the double attention convolution LSTM model by adopting multiple indexes. According to the scheme, the influence of features and time on the prediction accuracy is fully considered, and the prediction accuracy is improved.

The invention discloses a drill string vibration identification and regulation method based on multi-modal data fusion, and belongs to the crossing field of petroleum drilling and artificial intelligence. According to the method, multi-modal input fusing space and time-frequency information is constructed by using a three-axis vibration acceleration signal of an underground drill string and a three-channel time-frequency diagram corresponding to the three-axis vibration acceleration signal. Through a deep neural network in which BiGRU and ResNet18-SENet are combined, an attention mechanism is fused, and accurate recognition of complex vibration modes such as general vibration, stick-slip and vortex motion is realized. Further combining the recognition result, driving the self-adaptive regulation and control of drilling parameters, and realizing the closed-loop control of the vibration state of the drill string. The method has the advantages of being high in recognition precision, high in robustness, intelligent in regulation and control and the like, and is suitable for underground vibration monitoring and drilling parameter optimization in a complex stratum environment.

The invention discloses a drilling trajectory real-time dynamic tracking control method and system based on deep learning, and relates to the technical field of petroleum drilling, and the method comprises the steps: generating well trajectory control parameters according to the basic information of a well and preset well trajectory data; setting an optimal drilling fluid performance parameter corresponding to the current working condition according to the stratum environment where the drilling tool is located; deep learning is conducted on the well track control parameters and the optimal drilling fluid performance parameters through the correlation model, a drilling parameter setting scheme is generated, and an underground drilling tool is controlled to conduct drilling; comparing the drilling data with data in the drilling parameter setting scheme, and determining deviation in the drilling data; and determining a corresponding fault mode, and determining a corresponding solution from the solution library to correct the drilling parameter setting scheme. According to the method, deep learning is carried out on geological conditions and historical data, the well trajectory control model and other models are constructed, the model can be continuously updated and optimized along with data accumulation, and the drilling trajectory tracking and control precision is continuously improved.

The invention discloses a directional drilling track accurate control technology based on machine learning, and relates to the technical field of drilling engineering. The directional drilling track accurate control technology comprises the following steps that underground parameters of a drilling tool during underground operation are obtained; establishing a dynamic model based on the drilling tool structure and the motion state; based on the dynamic model, introducing a long-short-term memory neural network, and constructing a hybrid prediction model; drilling parameters are input into the hybrid prediction model, and trend information of multiple tracks is output; based on current geological conditions, drilling tool configuration and operation safety constraints, performing simulation evaluation on the trend information of the plurality of candidate tracks, and screening out an optimal track meeting track precision and underground safety requirements from the candidate tracks; based on the optimal track, control strategy input is constructed, and a state space containing a tool face angle, target azimuth deviation and a drilling tool state is set; and through a deep reinforcement learning method, an advanced adjustment instruction for the guiding tool is generated, and drilling operation is executed according to the optimal track and the advanced adjustment instruction.

The invention discloses a multi-well linkage drilling parameter multi-level optimization decision-making method and system, and the method comprises the steps: obtaining real-time drilling data in a drilling operation process, so as to determine a first rock breaking parameter; under the condition that the first rock breaking parameter is out of the preset range, the mechanical drilling speed corresponding to the real-time drilling data is predicted according to a preset model; generating a plurality of candidate parameter groups according to the mechanical drilling speed and the drilling constraint condition, and determining a function value corresponding to each candidate parameter group based on a multi-objective optimized objective function; determining a target parameter group in the plurality of candidate parameter groups according to the function value of each candidate parameter group; and target drilling working parameters are determined according to the target parameter set, and the target drilling working parameters serve as current drilling working parameters for drilling operation. According to the scheme, the drilling working parameters are intelligently optimized by using the multi-objective optimization algorithm while complex working conditions are avoided, and the drilling efficiency is comprehensively improved.

Methods and systems are disclosed that use a temperature sensor integral to a drill bit during drilling. The temperature sensor is configured to measure temperature associated with a cutting element of the drill bit over time during drilling. The temperature sensor is used to generate temperature data representing temperature of the cutting element of the drill bit over time during drilling. The temperature data is processed to determine and monitor at least one condition of the wellbore and / or the drill bit during drilling, such as i) detection of faults or openings or cracks or other geological rock features of the wellbore being drilled, ii) estimation of one or more drilling parameters of the drill bit (such as rate of penetration (ROP), iii) detection of lost cutting element(s), iv) detection of bit balling, and v) detection of drilling efficiency.

The embodiment of the invention relates to the technical field of petroleum and natural gas engineering, in particular to a drifting drilling tool determining method, device and equipment, and the method comprises the steps that first drilling parameter data corresponding to a drifting drilling tool and second drilling parameter data corresponding to a borehole casing are obtained; calculating a first blocking risk factor of the drifting drilling tool according to the first drilling parameter data, wherein the first blocking risk factor is used for representing the blocking possibility of the drifting drilling tool; calculating a second blocking risk factor of the borehole casing according to the second drilling parameter data, wherein the second blocking risk factor is used for representing the blocking possibility of the borehole casing; calculating a first tripping-in capability index of the drifting drilling tool according to the rigidity of the drifting drilling tool and the first blocking risk factor; calculating a second tripping-in capability index of the borehole casing according to the rigidity of the borehole casing and the second blocking risk factor; and according to the first tripping-in capability index and the second tripping-in capability index, whether the drifting drilling tool is matched with the borehole casing is determined.

The invention provides a drilling risk processing method and device based on a large language model. On the basis of the method, firstly, current drilling state parameters of a target well are obtained, and state sensing data matched with the current drilling state of the target well are determined according to the current drilling state parameters; textualization processing is carried out according to the state sensing data, and a current drilling state text of the target well is obtained; determining target knowledge data matched with the current drilling state of the target well by utilizing the current drilling state text of the target well and combining a preset external knowledge base; generating a target prompt word for the current drilling state of the target well according to the target knowledge data and the current drilling state text of the target well; according to the method, whether the target well has the drilling risk currently or not is efficiently and accurately determined by processing the target cue word through a preset large language model, and corresponding risk reason explanation and / or risk processing measures are determined under the condition that it is determined that the target well has the drilling risk currently.

The invention discloses an offshore oil and gas resource efficient evaluation method based on a well-free / few-well condition, and the method comprises the steps: S10, carrying out the collection of seabed multi-field coupling data, and obtaining an original data flow; s20, performing multi-source data intelligent fusion processing: performing data space-time alignment, performing electromagnetic seismic joint inversion by using a joint objective function, performing leakage path analysis, and extracting reservoir physical property parameters and a leakage network; s30, dynamic geological knowledge graph construction: constructing a graph neural network and establishing a cross-regional reservoir parameter prediction model through transfer learning to obtain a dynamic geological model and transfer learning parameters; s40, virtual well intelligent generation: guiding virtual well generation to obtain a virtual well data set; and S50, reservoir modeling evaluation: training reservoir modeling together with the virtual well data and the real seismic attributes to obtain resource probability distribution and sweet spot division. According to the method, the dependence on dense drilling is reduced, the defect of multiplicity of solutions of geophysical data is overcome, and the technical bottleneck of modeling under the condition of less wells / no wells is broken through.

A system and method of real-time optimization of drilling performance metrics during a well drilling operation, for oil and gas as well as geothermal wells, or wells drilled for any other purpose. In a preferred form, the system receives information about allowable drilling metrics and real-time information of performance indicators. The drilling performance metrics and performance indicators are used to build a model to predict drilling parameters likely to optimize one or more drilling performance metrics.

The embodiment of the invention provides an underground tool face dynamic control method and system based on reinforcement learning, and belongs to the technical field of directional drilling. The method comprises the steps of collecting drilling state data of a target well, performing data preprocessing on the drilling state data, and constructing a corresponding state vector based on the preprocessed drilling state data; calling an action decision model obtained by training based on a DDPG algorithm based on the state vector to obtain an action instruction; wherein the network strategy parameter of the action decision model is obtained by correcting the previous network strategy parameter based on the deviation between the current drilling state data and a pre-constructed reward function; analyzing the action instruction to determine a target physical action; and executing top drive torsional pendulum based on the target physical action, and recycling an execution result until the execution result indicates that the tool face angle meets the expectation. According to the scheme, the stability and the real-time control effect under the working conditions of complex well sections and severe disturbance are improved.

The invention provides an intelligent teaching-assistant system base LLM training method for a drilling simulator, and belongs to the technical field of petroleum drilling large language model training, and the method comprises the steps: obtaining the simulation data of the drilling simulator based on an intelligent teaching-assistant system base large language model, and carrying out the field self-adaptive pre-training; performing fine adjustment on the original weight by utilizing low-rank self-adaption, and performing operation steps and fault diagnosis generation; the method comprises the following steps: constructing an enterprise private knowledge base, training a large language model by utilizing retrieval enhancement generation, and obtaining a trained verification header through explicit supervision; a direct preference optimization loss function is defined, a direct preference optimization loss function of retrieval perception is obtained in combination with the verification head, the large language model is optimized, and large language model training is completed; according to the invention, an external professional knowledge base can be fused, an equipment operation mechanism can be understood, an intelligent assistant architecture with teaching guidance and error diagnosis capabilities is provided, and the intelligent level of the drilling simulator is improved.

The invention belongs to the technical field of drilling fluid, and particularly relates to a free radical polymerization water-based drilling fluid composition and a preparation method thereof. The invention relates to a core-shell composite particle-containing water reducer which comprises deionized water, salts (NaCl / KCl), alkalis (Na2CO3 and NaOH), barium sulfate and core-shell composite particles X. The particle is composed of a silicon dioxide core and a polymer shell layer, and the shell layer is formed by free radical copolymerization of acrylamide, 2-acrylamido-2-methylpropanesulfonic acid and acrylic acid and is covalently connected with the core through silanization treatment. Under the conditions of high salinity, strong alkali and high solid phase weighting, the drilling fluid simultaneously realizes the following effects: (1) high-density bearing and low-viscosity pumpability are realized; (2) compact plugging and water loss reduction and low formation damage flowback can be realized; and (3) stable rheology rock carrying, lubrication and friction resistance reduction. The problem that three aspects of performance are difficult to consider in the prior art is solved, and the drilling fluid is suitable for drilling of deep wells, ultra-deep wells and complex stratums and has remarkable industrial application value.

The invention discloses an intelligent drilling mud pump, and relates to the technical field of mud pump devices, the intelligent drilling mud pump comprises a supporting frame body, the top of the supporting frame body is provided with a mud pump main body, and the mud pump main body is composed of two sets of cylinder barrels, a power assembly, two sets of reciprocating pressure pumping assemblies, a feeding pipe and a discharging pipe. According to the intelligent drilling mud pump, the displacement and efficiency of the pump are improved, the reciprocating frequency of the pump can be reduced through a bidirectional working mode, energy consumption can be reduced, then it can be guaranteed that under the condition that the speed of the power assembly is not changed, reciprocating motion of the reciprocating pressure pumping assembly can do work, single-stroke work of the reciprocating pressure pumping assembly is changed into double-stroke work, and the working efficiency is improved. Therefore, the slurry conveying efficiency can be improved and the cost can be reduced under the condition that the speed of the slurry pump is not changed, meanwhile, in the slurry conveying process, sand and stones in the conveyed slurry can be filtered through the filtering and cleaning assembly, the intercepted sand and stones can be cleaned in time, and the slurry conveying quality can be guaranteed.

The invention relates to the technical field of rotary steerable drilling system dynamics analysis and control, in particular to a rotary steerable drilling system dynamics analysis system which comprises a data acquisition module used for acquiring ground engineering parameters and underground dynamic parameters in real time and intercepting drilling parameter adjusting instructions; the digital twin modeling module is used for outputting a complete state vector of a digital twin model; the noise prediction module is used for generating a control source noise signal; the signal reconstruction module is used for executing self-adaptive hedging processing on the original mixed signal so as to reconstruct a pure geological signal; the cooperative control module is used for predicting the vibration risk caused by the change of the front stratum and generating an optimal control instruction for actively avoiding the vibration risk; the occurrence probability of malignant vibration is remarkably reduced, the drilling efficiency is improved, and the service life of a drilling tool is prolonged.

The invention relates to the technical field of petroleum drilling engineering, and particularly discloses an analysis and prediction system based on petroleum drilling and production data, which realizes virtual reconstruction of a physical drilling system by constructing a digital mapping body, and adopts a multi-source sensor network to collect drill string vortex frequency and wellbore temperature field gradient data. Forming a time sequence feature set through time sequence alignment and fusion processing; performing differential transformation and modal decomposition on the feature set to extract transient response feature vectors and generate a risk distribution diagram; establishing a parameter-risk correlation model, and decoupling mechanical vibration and thermal stress interference by solving a physical equation to obtain a dynamic risk index; the risk indexes and the process parameters are mapped to a three-dimensional grid to construct a multi-dimensional feature space, a feature fusion grid is adopted to achieve information aggregation and reconstruction, and comprehensive risk assessment indexes are generated; a dynamic early warning threshold value is established according to the evaluation indexes, and drilling parameters are optimized in real time through closed-loop control.

The invention relates to the technical field of parameter optimization, in particular to a data-driven hydraulic parameter real-time optimization method and system, and the method comprises the steps: obtaining drilling multi-source heterogeneous data, and carrying out the cleaning and multi-modal space-time alignment fusion, and generating a standardized multiphase flow parameter; inputting a pre-trained multiphase flow transient model through a data-model combined driving mechanism, dynamically correcting fluid density distribution and phase change parameters, and outputting a transient simulation result matched with a drilling working condition in real time; and determining hydraulic parameters to be optimized based on a simulation result, and generating a real-time optimization scheme of optimal displacement, throttling pressure and well killing parameters by combining wellbore pressure constraint and a multi-objective optimization function and adopting an adaptive particle swarm optimization algorithm for iterative calculation. According to the method, multi-source data space-time alignment and model dynamic correction are achieved, transient simulation precision is improved through a combined driving mechanism, and an optimal hydraulic parameter scheme meeting wellbore safety constraints can be generated by combining an adaptive optimization algorithm.

The invention relates to a preparation method and application of high-temperature-resistant organic modified bentonite. According to the method, three organic modifiers of long-chain quaternary ammonium salt, amide and a silane coupling agent are compounded to modify bentonite, and the problem that a single modifier is easy to decompose at high temperature is solved through the synergistic effect of physical intercalation, chemical crosslinking and interface bonding; and secondly, a three-dimensional stable structure is constructed by adding nano silicon dioxide and a composite modifier, so that the high temperature resistance and dispersion stability of the modified bentonite are further enhanced. According to the invention, the requirements of deep well and ultra-deep well drilling fluids on the temperature resistance and rheological property of the treating agent can be met, and the ultra-deep well drilling fluid which can tolerate the high temperature of 250-300 DEG C, and is low in filter loss and good in dispersion stability can be obtained.

This disclosure relates to a predictive lookahead system that generates simulated or predicted wellbore trajectory plans in a drilling environment and determines when a simulated wellbore trajectory plan is at risk based on various lookahead metrics, such as torque and drag (T&D) metrics and hydraulic pressure metrics. For instance, the predictive lookahead system uses a predictive framework with various steps to determine if drilling metrics for a predictive wellbore trajectory plan, such as T&D metrics and / or hydraulic pressure metrics corresponding to subsurface formations, may exceed one or more risk threshold limits and cause damage to the drill bit, drill string, casing, and / or surface rig. The predictive lookahead system determines whether a predictive wellbore trajectory, which aims to converge with a previously planned wellbore trajectory, can proceed safely along the projected trajectory without exceeding T&D, pressure window, or other lookahead metric limits in the wellbore.

A method may include performing, by a drilling system, a drilling operation to produce a wellbore in a geological region of interest. The method may further include determining time elapse data describing an amount of time between the drilling operation and a hydraulic stimulation operation. The method may further include determining borehole stress data based on the wellbore, reservoir data, and geological data. The method may further include determining thermal diffusivity data regarding a temperature front in the geological region of interest based on the time elapse data, the reservoir data, and the geological data. The method may further include determining a wellbore breakdown pressure of the geological region of interest based on the thermal diffusivity data, the geological data, the borehole stress data, and the reservoir data. The method may further include transmitting a command to a stimulation control system based on the wellbore breakdown pressure.