Drilling Dysfunction Probability Estimation via Simulation
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Solution Overview
Problem
Drilling dysfunctions such as stick-slip and whirl, caused by a combination of forces acting on the drill string, lead to equipment damage and increased costs due to downtime, and there is a need for predictive methods to anticipate these issues with certainty.
Innovation Solution
A method using a mathematical model of a drill string to perform simulations with drilling-related data and parameters, estimating the probability of drilling dysfunctions occurring, and optimizing drilling parameters to prevent such issues, which includes entering data into a processor for simulations, selecting parameters that minimize dysfunction probabilities, and transmitting these to a signal receiving device for control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If drilling simulations are performed using deterministic models, then computational efficiency is maintained, but the uncertainty and probability of drilling dysfunctions cannot be accurately predicted
Solution Approach 1:
The patent applies preliminary action by pre-defining probability distribution functions for uncertain parameters before running simulations. These distribution functions (normal, uniform, triangular, etc.) are established based on historical data and expert knowledge, allowing the system to systematically explore parameter uncertainties without ad-hoc adjustments during simulation execution.
Solution Approach 2:
The patent implements dynamics by transitioning from static deterministic parameter values to dynamic probabilistic parameter distributions. The simulation model dynamically samples parameters from their respective distribution functions across multiple simulation runs, enabling the system to capture the evolving uncertainty and predict dysfunction probabilities rather than single deterministic outcomes.
2Reliability
If multiple drilling simulations with varying parameters are performed to assess uncertainty, then prediction reliability improves, but computational time and resources increase
Solution Approach 1:
The patent applies partial action by performing a limited number of simulations (e.g., 10-100 runs) rather than exhaustive sampling. This partial sampling approach provides sufficient statistical confidence for practical decision-making while avoiding the computational burden of exhaustive Monte Carlo analysis, achieving an optimal balance between reliability and computational efficiency.
Solution Approach 2:
The patent implements parameter changes by systematically varying drilling parameters (RPM, WOB, feed rate) across their probability distributions in multiple simulation runs. This approach captures the combined effect of parameter uncertainties on drilling stability, enabling reliable probability predictions through controlled parametric variation rather than exhaustive exploration.
3Stability of the object's composition
If drilling parameters are optimized to minimize dysfunction probability, then drilling stability improves, but drilling productivity may be reduced due to conservative parameter selection
Solution Approach 1:
The patent applies parameter changes by identifying optimal drilling parameters that minimize dysfunction probability while maintaining acceptable productivity levels. The optimization process adjusts parameters such as RPM, WOB, and feed rate within their feasible ranges to find the sweet spot where stability is maximized without excessively compromising drilling rate, enabling data-driven parameter selection.
Solution Approach 2:
The patent implements feedback by using simulation results to iteratively refine drilling parameter recommendations. The system analyzes simulation outcomes, identifies parameter combinations that lead to dysfunctions, and provides feedback to adjust parameters toward optimal values, creating a closed-loop optimization process that continuously improves drilling stability based on predicted performance.
Data Source
AI summary
A method for estimating a probability of a drilling dysfunction or a drilling performance indicator value occurring includes entering drilling-related data having a probability distribution into a mathematical model of a drill string drilling a borehole penetrating the earth and entering drilling parameters into the model for drilling the borehole. The method further includes performing a plurality of drilling simulations using the model, each simulation providing a probability of the drilling dysfunction occurring or a probability of a drilling performance indicator value occurring with associated drilling parameters used in the simulation, selecting a set of drilling parameters that optimizes a drilling objective using the probabilities of the drilling dysfunction occurring or the probabilities of a drilling performance indicator value occurring; and transmitting the selected set of drilling parameters to a signal receiving device.