AI Wellbore Fluid Management for Real-Time Rheology Control
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Solution Overview
Problem
Current methods for managing drilling fluid properties in oil and gas exploration rely heavily on experience and computer models, which often lack precision due to incomplete knowledge of fluid composition and fail to accurately anticipate the impact of additives, limiting pilot tests to a few simulations and focusing on sample volumes rather than full-scale operations.
Innovation Solution
Integration of artificial intelligence techniques, such as artificial neural networks, with physical models to predict and manage wellbore fluid properties in real-time, using closed-form solutions to determine viscosity changes and chemical interactions, enabling real-time corrective actions and additive treatments to maintain target rheological values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If computer models are used to predict fluid property changes, then the speed of decision-making is improved, but the measurement precision and reliability of predictions deteriorate due to incomplete knowledge of fluid composition
Solution Approach 1:
The system implements a feedback mechanism where actual fluid property measurements from sensors are continuously compared with model predictions. The differences (residuals) are used to update and refine the computer models in real-time, improving prediction accuracy while maintaining fast decision-making speeds. This closed-loop feedback system allows the models to learn from actual data and adapt to the specific fluid composition being used.
Solution Approach 2:
The system enables self-service through automated real-time monitoring and prediction, where the computer models continuously predict fluid property changes without requiring manual pilot tests. The system serves itself by automatically detecting when fluid properties deviate from targets and recommending corrective actions, eliminating the need for continuous human intervention and expensive physical testing.
2Reliability
If pilot tests are conducted to evaluate additive impacts, then the reliability of predictions is improved, but the loss of time and productivity worsen due to limited testing capacity
Solution Approach 1:
The system creates virtual copies of pilot tests through computer simulations that replicate fluid behavior based on measured composition data. Instead of conducting physical pilot tests on small samples, the system uses calibrated models to simulate additive impacts on the actual fluid system, providing reliable predictions without consuming time or limiting productivity. These virtual copies allow unlimited testing scenarios to be evaluated instantly.
Solution Approach 2:
The system performs preliminary actions by predicting additive impacts before actual additions are made. The computer models evaluate potential fluid property changes in advance, allowing engineers to select optimal additives and dosages before implementing them in the drilling fluid system. This preliminary virtual testing eliminates the need for time-consuming sequential pilot tests and maintains continuous drilling productivity.
3Measurement precision
If more pilot tests are conducted to improve model accuracy, then the measurement precision is improved, but the loss of time and resource consumption increase
Solution Approach 1:
The system applies partial action by conducting minimal necessary physical measurements (key fluid property sensors) while using computer models to evaluate the full range of additive impacts. Instead of performing exhaustive pilot tests for every possible additive scenario, the system measures essential parameters and lets the calibrated models predict the remaining properties, achieving high accuracy with minimal time investment.
Solution Approach 2:
The system replaces the mechanical pilot testing system with a computational prediction system. Physical pilot tests that require mixing, waiting, and remeasuring are substituted with instant computer simulations that calculate fluid property changes based on measured composition and additive characteristics. This substitution eliminates the time loss inherent in physical testing while maintaining or improving measurement precision through comprehensive model evaluation.
Data Source
AI summary
A corrective product addition treatment or surface treatment to a wellbore fluid is based on performing an artificial intelligence technique and/or a closed form solution with respect to one or more fluid properties. Pilot testing and operational testing to operatively dose a fluid at a drilling site may be integrated into the corrective product addition treatment or surface treatment to a wellbore fluid.