Geology and Geophysics Data Normalization for Completion Analytics
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Solution Overview
Problem
Current optimization and design processes in petroleum engineering, relying on geology and geophysics (G&G) data and completion data, are not comprehensive or efficient, leading to potential dominance of completion data over G&G data, which can result in inadequate optimization and design outcomes.
Innovation Solution
An apparatus and processing method that normalize production data based on both G&G data and completion data separately, using modules to generate and process input and reference signals, facilitating sweet-spot based machine learning (SSML) and completion-based machine learning (COMML) to mitigate data dominance and enhance optimization and design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If completion data is used for optimization and design, then design efficiency is improved, but completion data may dominate over G&G data leading to inadequate optimization outcomes
Solution Approach 1:
The patent segments the data processing into separate normalization modules: one for normalizing completion data and another for normalizing G&G data. This segmentation ensures that each data type is processed independently with appropriate weighting, preventing completion data from dominating the optimization process while maintaining design efficiency.
Solution Approach 2:
The patent applies parameter changes by introducing normalization factors and weighting parameters that adjust the influence of different data types. By changing the parameters of data processing (normalization based on respective data characteristics), the system balances the contribution of completion data and G&G data, ensuring reliable optimization outcomes without sacrificing efficiency.
2Reliability
If G&G data and completion data are integrated for analytics, then comprehensive optimization is achieved, but data processing complexity increases
Solution Approach 1:
The patent divides the complex data integration task into separate processing modules: a completion data normalization module and a G&G data normalization module. Each module handles specific data types with dedicated processing logic, reducing overall system complexity while achieving comprehensive optimization through integrated results.
Solution Approach 2:
The patent introduces normalization as an intermediary process between raw data collection and final analytics. The normalization modules act as mediators that standardize different data types (completion data and G&G data) into comparable formats, simplifying the integration process and reducing processing complexity while maintaining comprehensiveness.
3Reliability
If normalization is applied to balance data influence, then data dominance is mitigated, but processing time increases
Solution Approach 1:
The patent applies normalization as a preliminary action before the main analytics process. By pre-normalizing completion data and G&G data separately using their respective normalization modules, the system balances data influence in advance, preventing dominance issues during subsequent processing and reducing overall processing time.
Solution Approach 2:
The patent implements selective normalization focused on the most critical data types and parameters. Rather than normalizing all possible data equally, the system applies normalization partially to the key data sets (completion data and G&G data) that have dominance issues, achieving data balance without excessive processing time investment.
Data Source
AI summary
There is provided an apparatus which can include a first module which can be configured to receive at least one input signal and/or generate at least one input signal. The input signal(s) can include geology and geophysics (G&G) based data and completion data. Completion data can, for example, be associated with a structure. The first module can be further configured to receive at least one reference signal and/or generate at least one reference signal. The apparatus can further include a second module which can be coupled to the first module. The second module can be configured to process the input signal and the reference signal by manner of normalizing the reference signal based on G&G data and/or normalizing the reference signal based on completion data so as to produce at least one output signal corresponding to at least one normalized reference signal.