Drilling Optimization Using Neural Network Simulation
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Solution Overview
Problem
Current drilling technologies lack direct relationships between performance evaluating parameters and controllable drilling parameters, leading to inefficient drilling operations due to trial and error methods, which are time-consuming and often result in suboptimal conditions.
Innovation Solution
The method utilizes artificial neural networks (ANNs) to establish direct relationships between controllable drilling parameters (WOB, RPM, and hydraulic power) and performance evaluating parameters (SE and ROP), allowing for real-time optimization by continuously updating model parameters based on MWD data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If trial and error methods are used to determine drilling parameters, then operators can eventually find acceptable drilling conditions, but the process is time-consuming and results in suboptimal drilling performance
Solution Approach 1:
The patent applies preliminary action by pre-establishing the mathematical relationships between drilling parameters and performance metrics through neural network modeling before actual drilling operations. The system pre-processes MWD data to create predictive models that can immediately guide parameter selection, eliminating the need for time-consuming trial and error during drilling. This allows operators to directly apply optimized parameters from the start of each drilling segment.
Solution Approach 2:
The patent replaces the mechanical trial-and-error adjustment process with an intelligent information processing system. Neural networks and mathematical models substitute for manual operator experimentation, automatically calculating optimal drilling parameters based on real-time MWD data and pre-established relationships between parameters and performance metrics.
2Productivity
If drilling parameters are not optimized, then operators can maintain simple operational procedures, but drilling efficiency remains low and costs increase significantly
Solution Approach 1:
The system applies self-service by enabling the drilling operation to automatically optimize its own parameters through real-time data processing. The neural network models continuously analyze MWD data and self-adjust parameter recommendations without requiring external intervention or complex external control systems, making the optimization process inherent to the drilling operation itself.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting drilling parameters (WOB, RPM, hydraulic power) based on real-time MWD data and neural network predictions. The system continuously modifies these parameters to maintain optimal drilling conditions, transforming static operational procedures into dynamic, adaptive parameter control that responds to changing downhole conditions.
3Productivity
If higher ROP is achieved through increased energy spending, then drilling speed improves, but well bore stability deteriorates and bit life decreases
Solution Approach 1:
The patent applies feedback by continuously monitoring MWD parameters (torque, ROP, weight on bit) and using this real-time information to adjust drilling parameters through neural network models. The system feeds back performance data to automatically modify operational parameters, preventing excessive energy application that could compromise well bore stability or bit life while maintaining high ROP within safe operating limits.
Data Source
AI summary
The method disclosed receives a data stream from an MWD system and determines the response of a specific energy (SE) relationship and a rate of penetration (ROP) relationship respectively to variables controllable by the operator, in order to enable operation at a lowest SE, or a highest Rate-of-Penetration (ROP) to SE ratio. The method utilizes artificial neural networks trained by MWD data to deduce a depth-of-cut and torque based on relationships manifesting between the various data points collected, and an SE equation and a predicted ROP is evaluated over a series of probable operating points. The method continuously gathers and analyzes MWD data during the drilling operation and allows an operator to manage the controllable parameters such that operation at the lowest SE or highest ROP or ROP to SE ratio can be achieved during the drilling operation.