Drilling Parameter Optimization via Fourier Transform Decomposition
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Solution Overview
Problem
Current drilling technologies lack an efficient real-time method to optimize and automate drilling parameters, particularly for predicting and enhancing the Rate of Penetration (ROP), which is crucial for reducing drilling costs and improving efficiency.
Innovation Solution
A method utilizing historical drilling signals from multiple wells to generate optimized drilling parameters through Fourier Transform decomposition and recomposition, allowing for real-time prediction and automation of drilling activities, including the determination of optimal ROP and formation compressive strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If drilling parameters are maximized individually to enhance ROP, then drilling speed improves, but drilling cost increases and optimization efficiency decreases
Solution Approach 1:
The patent segments the complex ROP optimization problem into individual drilling parameter optimizations. Each drilling parameter (WOB, RPM, GPM, hook-load, torque, bit depth) is analyzed and optimized separately through signal filtering and evaluation, rather than attempting to optimize all parameters simultaneously. This segmentation reduces the complexity of the overall optimization system while still achieving enhanced ROP through coordinated parameter adjustments.
2Ease of operation
If real-time monitoring of multiple drilling parameters is implemented, then drilling activity optimization improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts and isolates individual drilling parameter signals from the complex multi-parameter monitoring system. Each parameter signal (WOB, RPM, GPM, hook-load, torque, bit depth) is separately filtered and evaluated to determine its specific contribution to ROP. This extraction approach simplifies the control system by treating each parameter independently rather than managing the full complexity of simultaneous multi-parameter interactions.
Solution Approach 2:
The patent introduces signal filtering as an intermediary process between raw drilling parameter measurements and ROP optimization decisions. Each drilling parameter signal passes through filtering mechanisms that isolate relevant information and remove noise, creating simplified intermediate representations that are easier to process and use for optimization purposes. This intermediary filtering layer reduces the complexity of direct multi-parameter analysis.
3Measurement precision
If historical drilling data from multiple wells is analyzed, then prediction accuracy improves, but data processing time and computational requirements increase
Solution Approach 1:
The patent performs preliminary filtering and evaluation of historical drilling parameter signals before conducting full ROP analysis. By pre-processing the signal data from multiple wells through filtering mechanisms and extracting key parameter contributions in advance, the system reduces the computational burden during actual prediction operations. This preliminary action on historical data maintains prediction accuracy while reducing real-time processing time.
Data Source
AI summary
A method may include obtaining a plurality of historical drilling signals for a plurality of wells and generating a plurality of drilling parameters from the plurality of historical drilling signals of drilling activities of the plurality of wells into one real-time database of a computer processor. The method further includes applying Fourier Transform to decompose a plurality of functions into the plurality of drilling parameters and determining an optimum drilling parameter based on one or more optimized drilling parameters. The method further includes recomposing the plurality of functions to automate drilling activities for new wells by generating trends of the one or more optimized drilling parameters and using the trends in the drilling activities. The method further includes creating Key Performance Indexes (KPIs) for the plurality of optimized drilling parameters to evaluate performance and monitor the drilling activities in real time using machine learning algorithms.