Search

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.

We have presented a finite-element based model for simulation of hydraulic fracturing. Each element is assigned strength in terms of a strain limit, and each element may have its individual strength. The element fractures when the strength limit is exceeded. One or more elements may break in one event. We show a 2D example of hydraulic fracturing of a low-permeable rock, where the fracture geometry is obtained. The bottom hole pressure is computed, which shows the pressure drops that follows each event. Finally, the magnitude of the fracture events is plotted in both space and time, which can be compared with the data from micro-seismic monitoring. A calibration of the model may provide effective parameter values for the rock. The presented formalism can also be coupled to reservoir simulators and calibrated against rupture propagation theories.

A method and system for identifying reservoir drainage patterns from microseismic data for illustrating flow paths towards ports. The method includes: determining moment tensor data for each of a plurality of microseismic events in the reservoir; inferring crack formation data in the reservoir in dependence on the moment tensor data; and calculating stream lines that represent predicted flow of fluids through the reservoir in dependence on the inferred crack formation data.

The success of microseismic monitoring campaign strongly depends on using the correct sensor layout. Detection limit and location accuracy are affected by the capability of a network to uniformly capture the energy emitted by seismic events in the subsurface. We here highlight the importance of using a accurate, 3D velocity model. We compare performance of a hypothetical network design using 1D and 3D velocity models. Our results show that the complexity of the 3D model translates into the key network performance indicators, such as distribution of magnitude of completeness, location accuracy and ground motion amplitudes.

We summarize observations of shear-wave splitting from passive seismic monitoring in a range of petroleum settings. Such measurements are relatively rare, but the emerging picture is one of fracture-induced anisotropy where a number of factors control both spatial and temporal variations. These observations offer insights into the state of stress in a reservoir and can serve as a ground-truth for geomechanical models.