Automated Well Model Calibration Workflow
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The oil and gas industry faces challenges in creating and calibrating well performance models efficiently due to lengthy processes, human errors, and the need for manual data retrieval from scattered databases, which consumes valuable engineer time and leads to inaccurate results.
Innovation Solution
A system and method for creating and calibrating production and injection well models through comprehensive data retrieval and automated workflow, using a user-friendly interface for data selection and calibration, applying scientific techniques to build accurate models quickly, and providing an interactive interface for customized calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data gathering and model calibration is performed by engineers, then model accuracy can be ensured through expert judgment, but the process takes 3-5 hours per well and is subject to human errors
Solution Approach 1:
The system performs self-service by automatically gathering data from multiple databases, validating data quality, building well performance models, and calibrating models without requiring continuous human intervention. The automated workflow executes the complete modeling process independently, eliminating manual data gathering and calibration steps while maintaining model accuracy through programmed validation rules and calibration algorithms.
Solution Approach 2:
The patent replaces the mechanical manual process of data gathering, validation, and model calibration with an automated computer-based system. The mechanical actions of engineers browsing databases, copying data, validating inputs, and adjusting model parameters are substituted by software automation that systematically performs these tasks using predefined rules and algorithms, dramatically reducing time while maintaining consistency.
2Reliability
If engineers manually gather and validate data from scattered databases, then data quality can be ensured, but the process is lengthy and consumes valuable engineer time
Solution Approach 1:
The automated system performs multiple functions within a single integrated workflow: it connects to and queries multiple scattered databases, automatically retrieves relevant data components, validates data quality against predefined criteria, and prepares data for modeling. This multi-functional automation replaces the engineer's manual activities of navigating different databases, collecting data, and validating quality, thereby maintaining reliability while dramatically improving productivity.
Solution Approach 2:
The patent introduces an intermediary automated workflow system that acts as a mediator between scattered databases and the well performance modeling process. This intermediary automatically queries databases, retrieves data, validates quality, and transfers processed data to the modeling engine, eliminating the need for engineers to manually intervene in data gathering and validation tasks while ensuring data quality through programmed validation rules.
3Reliability
If comprehensive data validation and QC processes are implemented, then model reliability is improved, but the process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary validation and quality control checks automatically during the data gathering phase, before data is passed to the modeling process. Data quality criteria are predefined and applied systematically to filter and validate data components, ensuring model reliability is established early in the workflow rather than requiring complex post-processing validation steps.
Solution Approach 2:
The automated workflow incorporates feedback mechanisms where validation results are automatically used to determine subsequent processing steps. If data fails validation criteria, the system automatically identifies and flags the issue, allowing for corrective action before proceeding to model building. This feedback loop ensures model reliability while keeping the process structure clear and manageable through automated decision logic.
4Productivity
If automated workflows are implemented, then processing speed increases dramatically, but the system requires sophisticated software integration and automation capabilities
Solution Approach 1:
The automated workflow is segmented into distinct modular steps: data gathering from databases, data validation against quality criteria, model building using validated data, and model calibration. Each segment is independently programmed and executed in sequence, allowing the complex overall process to be managed through manageable modules while achieving high automation speed and productivity.
Data Source
AI summary
Methods for creating and calibrating production and injection well models for a reservoir, are provided. An example of a method for creating and calibrating well models can include performing a comprehensive retrieval or gathering of required data components, feeding the gathered data into well performance software to thereby develop a model of the well, performing an initial calibration of the well model, performing a total system calibration on the well model, and performing a recalibration to fine tune the well model.