Real-time passive monitoring with a fibre-optic ocean bottom array

Abstract

To achieve high recovery rates, modern-day production management can benefit from not only snapshot images of the state of the reservoir at regular time intervals, but also continuous monitoring of the dynamic processes induced by pressure changes and fluid movement during production. Production management using time-lapse 4D snapshots is reactive, i.e., adjustments addressing the sweep efficiency or reservoir integrity can only be instigated once the next snapshot image is available after acquisition, processing and interpretation, often years later. For a more proactive reservoir management, it is important to have dynamic reservoir information in real time between the seismic time-lapse snapshots. Such information is contained in microseismic monitoring data and in surface or borehole deformation measurements. If sensors are permanently installed, this information comes at a negligible additional cost, provided that the data can be transferred to shore in real-time and processed automatically. Time-lapse 4D snapshot images are typically obtained over a period of years and are inadequately sampled for capturing dynamic reservoir changes taking place over much shorter time intervals, from hours to days. Such changes can include variations in the permeability caused by scaling or compaction (Barkved and Kristiansen, 2005), changes of the fluid phase owing to pressure variations (e.g., Osdal et al., 2006), unintended alterations of the flow paths owing to out-of-zone injections and fault reactivation (e.g., Schinelli et al., 2015), or movement in the overburden, potentially compromising the integrity of infrastructure in the form of casing failures or seafloor subsidence (e.g., Yudovich et al., 1989; Hatchell et al., 2017). While 4D seismic data can capture the cumulative effect of such processes by evaluating differences in still images every few years, they provide little information about when exactly the associated dynamic changes occurred and how they relate to changes in flow rate and pressure that may have been captured through continuous measurements in the wellbores accessing the reservoir. Microseismic events from within or around a producing reservoir can be indicative of reservoir fluid pathways and sub-seismic reservoir compartmentalization (e.g., Maxwell and Urbancic, 2001), or stress changes and associated production- related deformations in the vicinity (e.g., Teanby et al., 2004; Zoback and Zinke, 2002; Wuestefeld et al., 2011, 2013). Continuous monitoring of seismicity can also help in assessing deformation-related risks to infrastructure over the life of a field. Combined with pressure and flow rate, such data can provide the necessary information to capture dynamic processes in the reservoir right when they happen. In conjunction with geomechanical flow modelling, production optimization strategies can thus be validated and adjustments can be properly planned at an early stage. The result will be an improved sweep efficiency with a further increased recovery factor, as well as better risk assessment with the avoidance of potentially costly mitigation actions. A better understanding of, and continuous information about, the reservoir dynamics may even help to plan a 4D seismic strategy better. This can include better definition of suitable intervals for the acquisition of time-lapse images. These intervals could be irregular, depending on the state of reservoir development and type of recovery method. Continuous monitoring may also provide a means to high-grade areas of the reservoir for partial 4D imaging at lower cost and faster turnaround in between ‘full’ time-lapse surveys (Hatchell et al., 2013). In this paper we discuss the general ability to monitor microseismic events in an offshore setting and presents results from a real-time monitoring pilot in Norway. We validate the concept of continuous real-time monitoring from a fibre-optic deep-water installation by comparing our automatic detections with data from a regional seismograph network.