Drilling Parameter Optimization via AI Constraint Handling
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Solution Overview
Problem
Drilling operations face challenges in optimizing the rate of penetration (ROP) due to the complexity of drilling parameters and the risk of equipment failure, leading to non-productive time (NPT) and hazardous events, as existing methods rely on manual adjustments and derivative-based optimization which are inefficient in handling non-differentiable objective functions.
Innovation Solution
A method and system using constrained blackbox optimization based on derivative-free optimization, employing artificial intelligence to determine a non-linear relationship between ROP and drilling parameters, identifying user-specified constraints, and adjusting drilling parameters in real-time to maximize instantaneous ROP while minimizing NPT, utilizing sensors and machine learning algorithms to analyze measurement data and optimize drilling performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adjustment of drilling parameters is used to optimize ROP, then drilling performance can be improved, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical adjustment of drilling parameters with an automated computer-based optimization system that uses algorithms to determine optimal ROP, WOB, and RPM values, eliminating the time-consuming manual trial-and-error process
Solution Approach 2:
The optimization system automatically monitors drilling conditions and adjusts parameters without continuous human intervention, allowing the system to self-optimize drilling performance based on real-time data from sensors and measurement devices
2Productivity
If derivative-based optimization methods are used, then optimization can be performed, but they are slow and underperform with non-differentiable objective functions
Solution Approach 1:
The patent substitutes traditional derivative-based mathematical optimization methods with a neural network-based artificial intelligence system that can handle non-differentiable objective functions, providing both faster computation and better performance
3Productivity
If focus is placed on maximization of instantaneous ROP, then drilling speed increases, but equipment failure is likely resulting in hazardous events and non-productive time
Solution Approach 1:
The patent implements a feedback mechanism where the optimization system continuously monitors drilling parameters and equipment conditions, adjusting ROP, WOB, and RPM in real-time to maintain optimal performance while preventing equipment overload and failure
Solution Approach 2:
The system dynamically adjusts drilling parameters based on real-time conditions rather than using fixed settings, allowing instantaneous ROP to be maximized when conditions permit while automatically reducing parameters when equipment stress indicators suggest potential failure risks
Data Source
AI summary
A method for optimizing drilling performance is disclosed. The method includes determining, while advancing a drill bit during a drilling operation based on drilling parameters specified by a user, a rate of penetration (ROP), acquiring, using sensors disposed on drilling equipment of a well, measurement data of each drilling equipment that represents a condition of a corresponding drilling equipment at a particular ROP during the drilling operation, determining, using an artificial intelligence method based on the measurement data, a non-linear relationship between the ROP, the drilling parameters, and the conditions of the drilling equipment, identifying a constraint specified by the user based on the conditions of the drilling equipment, determining, based on the non-linear relationship and the user specified constraint, a target value of the drilling parameters to optimize a pre-determined performance measure of the drilling operation, and further performing the drilling operation based on the target value.