Probability-Based Cementing Procedure Recommendations for Zonal Isolation
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Solution Overview
Problem
Existing cementing operations in oil and gas wells face challenges in selecting the optimal blend of cement, pumping equipment, and downhole tools due to subjective and conflicting methodologies, leading to uncertainty in achieving job objectives such as zonal isolation and cement quality.
Innovation Solution
A probability-based mathematical model is developed to predict the success of cementing job objectives by correlating historical data with best practices, recommending tailored cementing procedures to enhance the likelihood of achieving desired outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If subjective and conflicting methodologies are used for selecting cement blend, pumping equipment, and downhole tools, then the selection process is flexible and adaptable, but the reliability of achieving job objectives decreases
Solution Approach 1:
The patent transforms the subjective selection process into an objective one by changing the parameters from expert opinion-based choices to data-driven probability calculations. Historical job data is analyzed to determine statistical relationships between cementing parameters (cement type, pump rate, equipment selection) and job objectives (zonal isolation, cement quality), allowing flexible yet reliable selection through probability-based recommendations
Solution Approach 2:
The system incorporates feedback loops where historical job outcomes are continuously analyzed and fed back into the probability model. The model learns from past successes and failures, adjusting probability calculations based on accumulated data, thereby improving reliability while maintaining adaptability to different well conditions
2Reliability
If a systematic probability-based model is implemented, then the reliability of achieving job objectives improves, but the device complexity increases
Solution Approach 1:
The patent introduces a computer system as an intermediary between the complex probability model and the user. The computer automatically performs data analysis, probability calculations, and generates recommendations, shielding users from the underlying complexity while delivering reliable, data-driven cementing procedure designs
Solution Approach 2:
The manual, subjective methodology selection process is replaced with an automated computer-based system that uses mathematical probability models. This substitution eliminates the need for manual analysis of multiple conflicting methodologies, reducing operational complexity while enhancing reliability through consistent, data-driven decision-making
3Measurement precision
If historical data analysis is performed to develop probability models, then the precision of predicting job outcomes improves, but the loss of time in data processing increases
Solution Approach 1:
The system performs preliminary data analysis by pre-processing historical job data and establishing probability relationships in advance. Once the model is trained on historical data, it can quickly generate predictions for new jobs without requiring extensive real-time data processing, thus improving precision while minimizing time loss during actual cementing operations
Data Source
AI summary
A method of designing a cement pumping procedure of a wellbore isolation barrier includes using a design application that receives customer inputs, which include wellbore data and a job objective. The design application can retrieve a cement pumping procedure from a data source comprising a series of sequential steps to achieve the job objective. The design application can load the customer inputs into the cement pumping procedure, access a database of best practices, calculate a probability score for achieving the job objective based on a model, and recommend modifying one or more steps of the cement pumping procedure with one or more best practices in response to the probability score being below a threshold.


