Smart Polymer Bottom-Hole Temperature Sensing During Continuous Drilling
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Solution Overview
Problem
Accurate measurement of downhole temperatures during drilling operations is challenging due to extreme conditions, affecting drilling fluid viscosity and tool safety, and current methods like LWD tools provide non-continuous measurements requiring downtime.
Innovation Solution
Utilizing smart polymers and a camera at the shale shaker to capture images of temperature-responsive polymers in drilling mud, combined with machine-learning models to estimate downhole temperatures continuously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LWD tools are used to measure downhole temperature, then temperature data can be obtained, but measurements are non-continuous and require downtime
Solution Approach 1:
Temperature-sensitive polymers are introduced as intermediary substances that carry temperature information from the downhole environment to the surface. These polymers change their optical properties in response to temperature variations, enabling continuous temperature monitoring without interrupting drilling operations. The polymers mix with drilling fluid and travel through the wellbore, serving as mobile temperature sensors that provide ongoing data throughout the drilling process.
Solution Approach 2:
The patent replaces mechanical temperature measurement systems (LWD tools requiring physical deployment and retrieval) with an optical-chemical system. Temperature-sensitive polymers undergo optical property changes (fluorescence, absorption, or color changes) in response to temperature, allowing non-contact, continuous temperature monitoring through optical detection at the surface, thereby eliminating the need for mechanical intervention and downtime.
2Temperature
If drilling fluid temperature increases, then cooling function is improved, but viscosity decreases leading to poor hole cleaning
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring temperature-sensitive polymer properties in the drilling fluid and using this information to adjust drilling parameters in real-time. When temperature increases cause viscosity degradation, the system provides feedback signals to modify flow rates, pump pressures, or drilling speeds to compensate for reduced hole cleaning capability, maintaining reliable operation despite temperature variations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables continuous, intervention-less measurement of maximum downhole temperatures, ensuring safe drilling operations and informed parameter adjustments, enhancing wellbore integrity and efficiency.
Implementation Method 1
Units of smart polymers with heat sensitivity are inserted by a monitoring system into drilling fluid pumped into a well during a drilling operation. The smart polymers are configured to be triggered by exposure to increasing levels of heat experienced in the well.
Implementation Method 2
Continuous images and observed characteristics of returning mud exiting through an annulus of the well and containing the units of smart polymer are captured by a camera positioned at a sensing location and linked to the monitoring system.
Data Source
AI summary
Systems and methods include techniques for using smart polymers. Units of smart polymers with heat sensitivity are inserted by a monitoring system into drilling fluid pumped into a well. The smart polymers are configured to be triggered by exposure to increasing levels of heat. An insertion timestamp associated with each unit is stored. Each insertion timestamp indicates a time that each unit was inserted. Continuous images and observed characteristics of returning mud exiting through an annulus of the well and containing the units of smart polymer are captured by a camera positioned at a sensing location and linked to the monitoring system. An estimate of temperatures at a drill bit of the drilling operation is determined using continuous images, observed characteristics, and insertion timestamps, based at least in part on executing image processing algorithms, machine-learning models, and deep-learning models. Suggested changes to be made to drilling parameters are provided.