Wellbore Pressure Prediction Using Radial Diffusivity Regression
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Solution Overview
Problem
Current well control methods require lengthy shut-in periods to stabilize shut-in casing pressure (SICP) and shut-in drill pipe pressure (SIDPP) after a kick event, leading to potential inaccuracies and increased safety and nonproductive time risks due to gradual pressure build-up, especially in wells with low permeability formations.
Innovation Solution
A method using real-time pressure data from subterranean casing, drill pipe, and wellhead sensors to perform a parametric non-linear regression analysis based on the radial diffusivity equation, predicting stabilized pressures with a best-fit curve that significantly reduces the time needed to establish accurate SICP and SIDPP values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the well is kept shut in for many hours to allow pressures to stabilize, then the SICP and SIDPP readings become stable, but the time required increases significantly and accuracy may still be compromised due to very gradual pressure build-up
Solution Approach 1:
The system performs preliminary pressure measurements and regression analysis during the initial shut-in period to predict the stabilized pressure values before full stabilization occurs. This allows operators to prepare kill weight mud formulations in advance, reducing the total shut-in time required while maintaining accuracy.
Solution Approach 2:
The system continuously monitors pressure build-up and uses regression analysis to provide feedback on the predicted stabilized pressure values. This feedback mechanism allows operators to track the pressure evolution and confirm when the predicted values are achieved, eliminating the need for extended waiting periods.
2Loss of time
If the well is shut in for a short period, then the time loss is reduced, but the SICP and SIDPP readings remain unstable and inaccurate
Solution Approach 1:
The system performs preliminary pressure measurements and regression analysis during the initial shut-in period to predict the stabilized pressure values before full stabilization occurs. This allows operators to prepare kill weight mud formulations in advance, reducing the total shut-in time required while maintaining accuracy.
Solution Approach 2:
The system replaces the traditional mechanical waiting period for pressure stabilization with a mathematical prediction model based on regression analysis. This substitution allows the system to estimate stabilized pressure values without requiring the physical time for pressures to naturally stabilize.
3Productivity
If inaccurate SICP values are chosen, then the well control process proceeds faster, but the well may be circulated in an underbalanced state increasing safety and environmental risks
Solution Approach 1:
The system continuously monitors pressure build-up and uses regression analysis to provide feedback on the predicted stabilized pressure values. This feedback mechanism allows operators to track the pressure evolution and confirm when the predicted values are achieved, eliminating the need for extended waiting periods.
Solution Approach 2:
The system performs preliminary pressure measurements and regression analysis during the initial shut-in period to predict the stabilized pressure values before full stabilization occurs. This allows operators to prepare kill weight mud formulations in advance, reducing the total shut-in time required while maintaining accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for accurate prediction of stabilized pressures within 10-15% of the time typically required by conventional methods, achieving a high R-squared fit and precise kill weight mud calculation, thereby enhancing safety and efficiency in well control operations by reducing underbalanced states and nonproductive time.
Implementation Method 1
The regression analysis is a parametric non-linear robust fitting regression performed around a form of the radial diffusivity equation.
Data Source
AI summary
Disclosed are systems and methods for predicting a stabilized pressure in a wellbore of a well after an undesired influx of formation fluids, i.e., a kick, into the wellbore in a real-time drilling operation. Following the kick, the well is shut in. Signals representing pressure data associated with the subterranean casing, drill pipe, wellhead, and/or the bottomhole assembly and associated time data are received in a processor. A regression analysis is performed using the pressure data and associated time data in the processor and solved for a predicted stabilized pressure associated with the subterranean casing, the drill pipe, the wellhead, and/or the bottomhole assembly respectively. The regression analysis is performed around a variant of the radial diffusivity equation describing the rate-pressure relationship for flow of a production fluid. The predicted stabilized pressure is communicated to a user.