Cement Slurry Design Using Predictive Models
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Solution Overview
Problem
Existing cementing methods face challenges in designing cement slurries that ensure well integrity and longevity, as they often require time-consuming and costly processes to predict material limits under downhole conditions.
Innovation Solution
A model-based approach is used to design cement slurry recipes by relating performance properties with barrier composition, curing, and testing conditions, utilizing cement performance property models to predict properties such as compressive strength, tensile strength, and cohesion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques are used to predict material limits of cement, then reliability of well integrity is improved, but loss of time and increased cost occur due to repeated sample curing and testing
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict cement material limits before actual downhole conditions are encountered. The models are trained on historical data and can forecast performance properties, allowing engineers to select appropriate cement compositions in advance without performing extensive preliminary curing and testing for each new application.
Solution Approach 2:
The patent uses copying by creating virtual models of cement behavior through machine learning. Instead of physically curing and testing numerous cement samples, the system creates digital replicas that simulate cement performance under various downhole conditions, allowing virtual testing and optimization before actual deployment.
2Reliability
If conventional techniques are used to predict material limits of cement, then reliability of well integrity is improved, but manufacturing complexity increases due to elaborate testing procedures
Solution Approach 1:
The patent replaces the mechanical testing system with a computational model. Instead of physically curing cement samples under controlled conditions and performing mechanical strength tests, the system uses machine learning algorithms to predict material limits, substituting complex physical testing infrastructure with software-based prediction.
Solution Approach 2:
The patent introduces an intermediary layer between cement composition selection and performance verification. The machine learning model acts as an intermediary that translates composition parameters into predicted performance properties, eliminating the need for direct physical testing while maintaining reliability through model-based prediction.
3Manufacturing precision
If extensive testing is performed to ensure cement performance, then manufacturing precision is improved, but productivity decreases due to repeated testing iterations
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on extensive datasets of cement compositions and their performance characteristics. This preliminary work enables rapid prediction of material limits for new compositions without requiring iterative physical testing, thus maintaining manufacturing precision while significantly improving productivity.
Solution Approach 2:
The patent uses copying by creating virtual cement samples through the machine learning model. Instead of physically manufacturing and testing numerous cement variations to achieve precise composition design, the system creates digital copies that can be rapidly evaluated, allowing precise composition optimization without the time cost of physical iteration.
Data Source
AI summary
A method of designing a cement slurry may include: (a) providing cement design requirements for the cement slurry wherein the cement design requirements comprise at least one cement performance property selected from the group consisting of compressive strength, tensile strength, cohesion, friction angle, Young's modulus, Poisson's ratio, and any combination thereof; (b) providing a virtual cement slurry recipe representing at least water and a concentration thereof and one or more cementitious materials and a concentration thereof; (c) inputting at least a well condition and the virtual cement slurry into a cement performance property model; (d) predicting performance properties for the virtual cement slurry recipe using at least the cement performance property model, wherein the performance properties comprise at least one of compressive strength, tensile strength, cohesion, friction angle, Young's modulus, and Poisson's ratio; (e) comparing the predicted performance properties to the cement design requirements; and (f) preparing a cement slurry according to the virtual cement slurry recipe if the predicted performance properties for the virtual cement slurry recipe satisfy the cement design requirements or repeating (b)-(f) if the virtual cement slurry recipe does not satisfy the cement design requirements, where the step of providing the virtual cement slurry recipe comprises providing a virtual cement slurry recipe with a disparate concentration of water, a disparate concentration of one or more of the cementitious materials, and/or a disparate chemical identity of the one or more cementitious materials.