Dynamic Basin Model for Drilling Prediction Uncertainty
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Solution Overview
Problem
Current drilling operations face challenges in accurately predicting the characteristics of subterranean formations ahead of the drilling process, leading to uncertainty and inefficiencies in optimizing drilling fluids and processes, which increases costs and risks.
Innovation Solution
A method and system that utilize a combination of basin and compaction models, integrated with geo-mechanical models, to predict subterranean formation characteristics, incorporating real-time, near-real-time, and lag-time sensor data from downhole sensors to reduce estimation uncertainty and optimize drilling parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional basin models are used to predict subterranean formation characteristics, then the prediction can be made, but the estimation uncertainty remains high and calculation time is long (hours)
Solution Approach 1:
The basin model is updated dynamically during drilling operations using real-time sensor data from the active borehole. The model transitions from a static pre-drilling prediction tool to a dynamic system that continuously adapts as new data becomes available, reducing both uncertainty and calculation time through iterative refinement
Solution Approach 2:
Sensor data collected during drilling operations provides feedback to the basin model, allowing continuous validation and adjustment of predictions. This feedback loop enables the system to reduce estimation uncertainty by comparing predicted characteristics with actual measurements and refining the model accordingly
2Measurement precision
If more sensor data and model integration are used to reduce uncertainty, then prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The integrated system serves multiple functions: it collects sensor data, updates the basin model, predicts formation characteristics, and provides drilling recommendations within a single unified platform. This multi-functionality reduces the need for separate systems while achieving high prediction accuracy through comprehensive data integration
3Measurement precision
If real-time sensor data is collected and used to update the basin model, then estimation uncertainty is reduced, but the data processing and model updating complexity increases
Solution Approach 1:
The basin model is developed and calibrated before drilling operations begin, establishing a baseline framework. This preliminary action allows the model to be ready for rapid updates during drilling without requiring complex real-time calculations, reducing processing complexity while maintaining accuracy
Data Source
AI summary
The disclosure presents processes for updating a prediction for the basin model and compaction model for a borehole. The results of the predicted parameters can be utilized by a drilling controller to adjust a drilling process, such as rotational speed, drilling fluid composition, drilling fluid additives, and other drilling process parameters. The predictions can be updated at various time intervals, such as real-time or near real-time for data collected by downhole sensors and a different time interval for sensor data collected at a lag time, such as cuttings analyzed by surface sensors. Throughout a drilling stage, the drilling process can be updated as new sensor data is received, allowing the uncertainty of the predictions to be reduced as new data is incorporated into the basin and compaction models, thereby enabling an increase in efficiency and optimization of the drilling process.