Insights Into the Physical Phenomena That Influence Automatic Gain/Loss Detection During Drilling Operations

Abstract

Summary Abnormal variations of the pit volume are a prime indicator of mud losses and formation-fluid influxes. However, the detection of gain and loss at the pit level can be difficult because of the transient behavior of several components in the hydraulic loop. For instance, while circulating, the increased pressure inside the drillstring and the annulus results in a larger amount of mud taken by the well compared with static conditions because of the compressibility of the drilling fluid. Furthermore, the transport and separation of cuttings also influence volume variations in the pit. Finally, the retention capacity of some of the mud-transport and -treatment equipment (e.g., return flowline, shakers, sand trap, degasser, transfer pit) has a direct impact on the active-pit level. All these effects are transient and can cause substantial variations of the active-pit volume that may interfere with any solutions attempting to automatically detect gains or losses. Most of these effects are well-known and are dealt with in a pragmatic way with fingerprinting between the current pit-volume variation and a reference pattern obtained under similar drilling conditions. Nevertheless, the fingerprinting method has its limitations when the current drilling-parameter sequence does not have an obvious reference pattern. Consequently, automatic gain/loss detection algorithms using pattern matching with previously observed transient periods may have difficulties reducing the number of false alarms to an acceptable level. Measurement of the flow rate out close to the outlet of the well is another way to detect gains and losses. It has the advantage of not being influenced by the side effects of mud-treatment equipment. However, it is an instantaneous value instead of being cumulative as is the pit volume, and therefore requires precise measurements to be reliable. The limitations of the two mostly used flow-rate-out sensors, the flow paddle and the Coriolis flowmeter, are discussed in Cayeux and Daireaux (2013). We present in this paper an analysis of the transient phenomena that limit the existing detection techniques, and explain how those effects can be accounted for with proper modeling. This leads to a significant extension of the domain of applicability of the traditional approaches.