Neural Network Drilling Parameter Optimization
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Solution Overview
Problem
The complexity of drilling operations, including changes in downhole conditions, makes it challenging for drilling operators to monitor and adjust parameters effectively, leading to inefficiencies in well planning and control.
Innovation Solution
The use of neural network models, specifically sliding window neural networks (SWNN) and recurrent deep neural networks (DNN) with Gated Recurrent Unit (GRU) cells, for real-time optimization of drilling parameters, such as weight-on-bit, rotational speed, and fluid injection rate, to predict and adjust operating variables like rate of penetration (ROP) and hydraulic mechanical specific energy (HMSE), incorporating Bayesian optimization for iterative improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time monitoring and adjustment of drilling parameters is implemented, then drilling efficiency and accuracy are improved, but the complexity of the control system and difficulty of detecting and measuring downhole conditions increase
Solution Approach 1:
The patent replaces complex mechanical control systems with neural network models that process sensor data to predict optimal drilling parameters. The neural network substitutes for complex real-time mechanical adjustments by using computational modeling to determine optimal parameters based on historical and real-time data, thereby reducing the complexity of the control system while maintaining high drilling efficiency.
Solution Approach 2:
The patent introduces neural network models as an intermediary between sensor data and drilling control decisions. This intermediary layer processes complex sensor readings from multiple downhole sensors, filters out noise, and translates them into actionable predictions for optimal drilling parameters, simplifying the overall control architecture while improving responsiveness to downhole conditions.
2Speed
If real-time optimization of drilling parameters is performed using neural network models, then the speed and accuracy of well path adjustments are improved, but the computational resources and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by training neural network models offline using historical drilling data and simulated scenarios. The models are pre-configured with optimal parameter relationships, allowing them to make rapid predictions during actual drilling operations without requiring intensive real-time computational resources. This pre-computation approach enables fast well path adjustments while minimizing real-time energy consumption.
Solution Approach 2:
The patent uses copies of historical drilling data and simulated downhole conditions to train neural network models. Instead of processing every possible real-time scenario from scratch, the system uses trained models that have learned from copied historical patterns, enabling rapid inference during actual operations. This copying approach significantly reduces real-time computational energy requirements while maintaining high response speed.
3Measurement precision
If multiple sensors are deployed to capture downhole operating parameters, then measurement precision is improved, but the complexity of data transmission and processing increases
Solution Approach 1:
The patent merges multiple sensor data streams into a unified neural network input structure. Instead of processing each sensor signal separately through complex individual pipelines, the neural network consolidates data from multiple sensors (temperature, pressure, vibration, etc.) into a single integrated prediction model, reducing overall processing complexity while maintaining high measurement precision through multi-parameter analysis.
Solution Approach 2:
The patent extracts and filters only the most relevant features from the complex sensor data using neural network techniques. By identifying and extracting key predictive features from the multitude of sensor readings, the system reduces data transmission and processing complexity while preserving measurement precision. The neural network automatically performs feature selection, eliminating the need for complex manual feature engineering and data filtering pipelines.
Data Source
AI summary
System and methods for optimizing parameters for drilling operations are provided. Real-time data including values for input variables associated with a current stage of a drilling operation along a planned well path are acquired. A neural network model is trained to produce an objective function defining a response value for at least one operating variable of the drilling operation. The response value for the operating variable is estimated based on the objective function produced by the trained neural network model. Stochastic optimization is applied to the estimated response value so as to produce an optimized response value for the operating variable. Values of controllable parameters are estimated for a subsequent stage of the drilling operation, based on the optimized response value of the operating variable. The subsequent stage of the drilling operation is performed based on the estimated values of the controllable parameters.