Early Kick Detection via Physics-Based State Space Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for detecting and managing kicks in oil and gas drilling operations are hindered by reliance on lagging indicators, human error, and the inability to promptly detect unplanned fluid influxes due to downhole conditions that are not readily observable by the human eye.
Innovation Solution
A cyber-physical well monitoring system that combines real-time measurements with physics-based state space models to estimate the probability and uncertainty of a kick occurrence, using sensors to gather data on mud pit volume, return flow, standpipe pressure, and other parameters, and applying probabilistic estimators for timely detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time sensors and physics-based models are deployed for kick detection, then detection accuracy and timeliness are improved, but device complexity and computational requirements increase
Solution Approach 1:
The monitoring system is segmented into multiple independent components: downhole sensors for local measurements, surface sensors for additional parameters, a physics-based model for prediction, and a probabilistic estimator for decision-making. This modular architecture improves detection accuracy while managing complexity through division of functions.
Solution Approach 2:
The physics-based model continuously predicts expected well conditions before kicks occur, establishing a baseline for comparison. This preliminary action enables the system to detect deviations indicating kicks earlier and more accurately, while the pre-computed model reduces real-time computational complexity.
2Loss of time
If downhole sensors are used to measure well conditions, then detection timeliness is improved, but device complexity and cost increase
Solution Approach 1:
The sensing function is segmented between downhole and surface locations. Downhole sensors measure critical parameters directly at the source for timely detection, while surface sensors provide supplementary measurements. This segmentation reduces the need for extensive downhole instrumentation while maintaining detection timeliness.
Solution Approach 2:
The physics-based model acts as an intermediary that processes downhole sensor data and predicts kick conditions. This mediator translates raw sensor measurements into actionable predictions, reducing the need for direct complex downhole sensing of all parameters and enabling timely kick detection through model-based inference.
Data Source
AI summary
A well monitoring system particularly useful in detecting kicks in the well includes a well, a well system, and a computing apparatus. The well defines a wellbore and the well system includes at least one sensor measuring at least one well condition. The computing apparatus hosts a well monitoring software component that performs a method to detect a kick in a well. The method includes: storing a set of real-time data from a measurement of a well condition by the sensor, the measurements being correlative to an unplanned fluid influx into the well; modeling the operation of the well with a physics-based, state space model of the well system to obtain an estimate of the well condition; and applying the real-time data set and the estimate to a probabilistic estimator to yield a probability of an occurrence of a kick and a confidence measure for the probability.