Machine Learning Cement Slurry Design for Thickening Time Prediction
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Solution Overview
Problem
The process of designing cement slurries for well cementing is inefficient and time-consuming, relying on trial and error due to the complex interaction of factors affecting thickening time, which varies with geographical differences in cementitious materials and additives, making it challenging to predict and achieve desired properties.
Innovation Solution
A machine learning model utilizing a geographic database and inventory of materials to determine cement slurry composition based on design parameters, such as thickening time, fluid loss control, and rheology, trained with test results to predict and validate slurry performance, reducing the need for iterative testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial and error approach is used to design cement slurry, then satisfactory material properties can be achieved, but the process becomes time-consuming and complex
Solution Approach 1:
The patent replaces the mechanical trial-and-error testing system with a computational machine learning model that predicts slurry properties. The system uses a computer-based model trained on historical data to calculate optimal slurry composition and thickening time, eliminating the need for iterative physical testing while maintaining reliable material properties.
Solution Approach 2:
The patent performs preliminary computational analysis using a machine learning model to determine optimal slurry design before actual cementing operations. The model predicts thickening time and composition requirements in advance, allowing engineers to prepare appropriate slurry formulations without time-consuming field testing.
2Reliability
If multiple cement slurries with varying additives are tested to meet engineering requirements, then desired material properties can be achieved, but the resulting slurry becomes complex
Solution Approach 1:
The patent uses a machine learning model to systematically adjust and optimize slurry composition parameters based on target properties. The model calculates precise additive concentrations and cement blend ratios needed to achieve desired thickening time and performance characteristics, simplifying the composition selection process while meeting all engineering requirements.
Solution Approach 2:
The patent creates a virtual model of cement slurry behavior that replicates real-world performance characteristics. The machine learning model is trained on historical slurry test data to accurately predict how different compositions will perform, allowing engineers to select optimal formulations through computational analysis rather than extensive physical testing.
3Ease of manufacture
If cement components vary by region, then local material availability is optimized, but the process of selecting correct slurry becomes more complicated
Solution Approach 1:
The patent incorporates regional variations in cement component properties into the machine learning model. The system is trained on location-specific data to account for differences in local cementitious materials and additives, allowing it to automatically adjust slurry formulations to work optimally with available regional materials while maintaining consistent performance standards.
Solution Approach 2:
The patent creates a universal machine learning model that can handle multiple regional variations in cement materials through a single system. The model is designed to process different material inventories and environmental conditions across various locations, providing consistent slurry design capabilities regardless of regional differences in material availability.
Data Source
AI summary
A method of designing a cement slurry comprising retrieving, by a design process, a material inventory comprising a geographic cement and a set of design parameters. Determine, by a model within the design process, a design slurry composition based on at least one of the set of design parameters. Determine with a model using a machine learning process, a predicted thickening time by comparing the design slurry composition, the material inventory, and the set of design parameters to a plurality of datasets within a geographic database. Generate a slurry design in response to a set of validation results of a test sample exceeding the threshold value. Place the slurry design into a wellbore with a pumping operation.