A Machine Learning Analysis to Relate Flow Pattern and Pressure Gradient During Gas Kicks Under Static Conditions

Abstract

AbstractWarning signs of possible kick during drilling operation can either be primary (flow rate increase and pit gain) or secondary (drilling break, pump pressure decrease,). Drillers rely on pressure data at the surface to determine in-situ downhole conditions while drilling. The surface pressure reading is always available and accessible. However, understanding or interpretation of this data is often ambiguous. This study analyses significant kick symptoms in the wellbore annulus while under shut-in conditions.We have tied several observed annular flow patterns to the measured pressure gradient during water- air, and water-carbon dioxide complex flow. This is based on experiments in a 140-ft high flow loop, with a hydraulic diameter of approximately 3 in. The experiments were carried out under static conditions to simulate kick occurrence when the drilling fluid is not flowing, typically the wellbore is shut-in. We used an Artificial Neural Network (ANN) and K-Means clustering approach for kick prognosis. We trained these Machine learning models to detect kick symptoms from pressure response and gas evolution data collected between the kick occurrence and the Wellhead.The Artificial Neural Network (ANN) approach was relatively fast with a negligible difference in accuracy when compared for air influx and carbon dioxide influx for kick prognosis. The ANN resulted in an accuracy of about 90% and 93% for air-based kick prognosis. While the accuracy was 92% and 94% for carbon dioxide-based influx. With K-mean clustering, the Silhouette score were 0.5 and 0.6 for the air and carbon dioxide influx respectively. The estimation of the influx size and type is strongly a function of the duration of kick and bottom hole underbalanced pressure. Based on visual analysis, pit gain, and pressure signals, the quantity of the mass influx significantly controls the flow pattern, pressure losses, and pressure gradient as the kick migrates to the surface. The resulting turbulent flow after the initial kick (After Taylor bubble flow) varied with duration of kick, average liquid flow rate, influx type, and drilling scenario. We have tied the surface pressure readings to the flow regimes to better visualize well control approach while drilling. This is based on relating the significant kick symptoms we observed to the pressure readings at multiple locations and time, then training the Deep learning models based on this data.Although computationally demanding, the Deep-Learning model can use the surface pressure data to relay annular flow patterns while drilling. This work provides an alternative and relatively accessible primary kick detection tool for drillers based on measured pressure responses at the surface.