Dimension Embedding for Well Completion Risk Advisory
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Solution Overview
Problem
Well completion operations face challenges such as motor failure, stalling, lost circulation, and mill deformation, which existing technologies struggle to address effectively, leading to inefficiencies and increased risks during gas and oil well production.
Innovation Solution
A computer-implemented method using nonlinear dimension embedding algorithms to analyze historical well completion data, generating scatter plots that identify similarities between new and past jobs, allowing for proactive adjustments in parameters like mill type and weight on bit to mitigate potential issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional supervised learning approaches are used to optimize well completion operations, then predictions of predefined targets such as cumulative production can be made, but the ability to identify similarities between new and completed jobs and prevent challenges is limited
Solution Approach 1:
The patent creates a challenges-successes-failures database that stores and replicates historical well completion data, including job parameters, challenges encountered, and outcomes. This copied historical information is then used through dimension embedding algorithms to identify similarities between new and past jobs, enabling proactive risk mitigation by learning from recorded challenges without needing to re-execute the same physical operations
Solution Approach 2:
The patent introduces dimension embedding algorithms and scatter plot visualizations as intermediary tools between historical data and decision-making. These intermediaries transform high-dimensional job parameter data into reduced-dimensional representations that reveal similarity patterns, allowing operators to indirectly assess job similarities and potential challenges without direct comparison of all raw parameters
2Measurement precision
If multiple job parameters are analyzed to identify similarities between jobs, then better predictive insights can be obtained, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent applies dimension embedding algorithms that transform high-dimensional job parameter data into reduced-dimensional scatter plot representations. This dimensionality reduction technique preserves the essential similarity relationships between jobs while converting complex multi-parameter data into visually interpretable 2D or 3D plots, making similarity analysis more manageable without losing critical information
Solution Approach 2:
The patent segments the complex data analysis task into distinct components: data collection and cleaning, dimension embedding algorithm application, scatter plot generation, and similarity assessment. This segmentation allows each component to be optimized independently and facilitates the use of specialized algorithms for different stages of the analysis process
Data Source
AI summary
Systems and methods include a method for providing plots for challenges, successes, and failures in well completions. A challenges-successes-failures database is created from historical data collected from past well completions. The database identifies: 1) challenges encountered during well completions, 2) corresponding successes and failures, and 3) job parameters used during well completions. A dimension embedding algorithm is selected to represent the data. Hyper-parameter tuning is performed on the algorithm. The dimension embedding model is generated and added to a system pipeline for a new well completion job. Nonlinear dimension embedding algorithms are run against data points in the cleaned and processed data using the challenges-successes-failures database and new job parameters entered in a user interface. Scatter plots of two-dimensional (2D) points are generated and labeled with each point's job parameters.