Flow Pattern, Pressure Gradient Relationship of Gas Kick Under Dynamic Conditions

Abstract

AbstractThe warning signs of possible kick during drilling operation can either be primary (flow rate increase and pit gain) or secondary (drilling break, pump pressure decrease, and stroke increase). Likewise, the drillers rely on the pressure readings at the surface to have an insight into 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 drilling/circulating.We have tied several observed annular flow patterns to the measured pressure, and flow data from the surface during water-air, and water-carbon dioxide complex flow. This is based on experiments using a 140 ft high tower lab, with a hydraulic diameter of about 3 in. The experiments have been carried out under dynamic conditions to simulate circulating drilling mud from the wellbore. We used both supervised and unsupervised learning techniques for flow regime identification and kick prognosis. These include an Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Trees, K-Means and Agglomerative Clustering. All the machine learning techniques used in this work made excellent predictions with accuracy greater than or equal to 90%. For the supervised learning, the decision tree gave the overall best results with an accuracy of 96% for air-influx cases and 98% for carbon dioxide influx cases. For the unsupervised learning, K-Means clustering was the best, with Silhouette scores ranging from about 0.7 to 0.8 for the rate data clusters, and 0.4 to 0.5 for pressure data clusters. The mass rate per hydraulic diameter and the mixture viscosity also resulted in the best type of clusters. This is because this approach accounts for the fluid properties, flow rate, and flow geometry.The estimation of the influx size and type is highly dependent on the duration of kick and the overbalance kick influx pressure. 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. Surface pressure readings can be tied to flow regime to better visualize well control approach while drilling.This works provides an alternative and easily accessible primary kick detection tool for drillers based on measured pressure responses at the surface. It also relates this pressure data to certain annular flow regime patterns to better tell the downhole story while drilling.