Digital Bottle Test Prediction for Demulsifier Composition Selection

The patent creates a virtual copy of the physical bottle test through a digital twin model. This virtual model replicates the complex emulsion breaking process, allowing multiple iterations and optimizations to be performed computationally rather than physically. The digital twin maintains the same input-output relationships as the actual bottle test but eliminates the time-consuming physical iteration cycle, thereby preserving measurement precision while dramatically reducing testing time.

The patent performs preliminary computational work by pre-training machine learning models on historical bottle test data before actual optimization needs arise. This preliminary action creates a ready-to-use digital framework that can quickly evaluate demulsifier compositions without requiring new physical tests. The pre-established digital twin and trained models enable rapid prediction and optimization, reducing the need for iterative physical testing while maintaining accuracy.

The patent develops a universal digital twin framework that can handle multiple different formation types and emulsion compositions through a single adaptable system. Rather than creating separate testing protocols for each formation type, the digital model universally processes various input parameters (oil composition, water cut, temperature, pressure) and provides optimized demulsifier recommendations. This multi-functional approach maintains high adaptability across different geological formations while reducing the complexity of managing multiple specialized testing procedures.

The patent transforms the complex chemical composition data of various formations into standardized numerical parameters that can be processed by the digital twin model. By converting diverse chemical makeup information into comparable parameter formats (molecular weight distributions, polarity indices, boiling point ranges), the system can adapt to different formations through parameter variation rather than procedural complexity. This allows the same digital framework to handle unique formation characteristics without increasing testing process complexity.

The patent implements a feedback-driven optimization system where the digital twin model continuously refines demulsifier recommendations based on predicted performance metrics and constraint satisfaction. The model incorporates feedback loops that evaluate water separation effectiveness predictions against environmental constraints and cost parameters, iteratively optimizing the demulsifier composition. This feedback mechanism ensures high reliability of water separation while systematically minimizing environmental impact by selecting greener chemical options that meet performance thresholds.

The patent utilizes parameter optimization to transition from traditional high-dosage demulsifier approaches to optimized lower-dosage formulations. By adjusting key parameters in the digital model (demulsifier concentration, molecular structure parameters, injection timing), the system identifies compositions that achieve effective water separation at reduced chemical dosages. This parameter optimization directly reduces environmental impact and cost while maintaining separation effectiveness, as the digital twin predicts and validates performance at these optimized parameter settings.