Well Production Model Submodel Splitting and Calibration
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Solution Overview
Problem
Current well production modeling processes are time-intensive and require significant manual effort to update and calibrate, especially when managing multiple wells, as they often lack efficient methods to split and validate models in response to changes in operating conditions.
Innovation Solution
A method that splits the well production model into submodels, such as a surface submodel and a combined vertical lift performance and inflow performance relationship submodel, or three submodels, and calibrates the model by identifying mismatched results and adjusting properties through a root-finding process to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the well production model is updated and calibrated manually, then the model accuracy is maintained, but the time and effort required increases significantly
Solution Approach 1:
The well production model is divided into multiple submodels (e.g., reservoir submodel, wellbore submodel, surface submodel) that can be updated and calibrated independently. This segmentation allows parallel processing and reduces the overall time required while maintaining model accuracy through targeted calibration of only the affected submodels.
Solution Approach 2:
The system automatically compares model predictions with actual well performance data and uses this feedback to trigger selective calibration of submodels. This feedback mechanism ensures model accuracy is maintained by calibrating only when and where needed, rather than performing full manual calibration cycles.
2Productivity
If the model is split into submodels, then the calibration process becomes more efficient, but the model complexity increases
Solution Approach 1:
The model is segmented into modular submodels with clearly defined interfaces and data exchange protocols. This structured segmentation improves calibration efficiency through targeted updates while managing complexity through standardized connections between submodels.
Solution Approach 2:
The submodel framework is designed with universal interfaces and standardized data formats that allow the same calibration methodology to be applied across different submodels. This universality reduces the complexity burden by providing a consistent approach rather than requiring custom solutions for each submodel.
3Reliability
If dedicated staff manually check and upload new models, then model validity is ensured, but the operational burden increases
Solution Approach 1:
The system implements automated feedback loops that continuously monitor well performance data and compare it against model predictions. This automated validation ensures model reliability by detecting deviations and triggering recalibration processes without requiring manual intervention from dedicated staff.
Solution Approach 2:
The model calibration and validation system operates autonomously, with automated processes for detecting model validity issues, triggering calibration workflows, and updating models. This self-service capability maintains model reliability while eliminating the operational burden of manual model management.
Data Source
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AI summary
Methods, computer-readable media, and computing systems for maintaining a well production model. The method includes receiving an update to a parameter of the model of a well, and updating the model based on the update. The method also includes splitting the model into at least two submodels after updating the model, and running the submodels to obtain results. The method further includes determining that the results obtained by running the submodels do not match, and in response to determining that the results obtained by running the submodels do not match, calibrating the model based on the update.