D3.2 – Source Characterisation Of Large Magnitude Events (Lme) And Their Occurrence In Space And Time
Creators: name: Zang, Arno
name: Goertz-Allmann, B
name: Deichmann, N
name: Oye, V
name: Bohnhoff, M
name: Jousset, Philippe
name: Kwiatek, G
name: Zhao, P
name: Catalli, F
name: Gischign V
Publisher: name: Zenodo
Year: 2012
Subjects: geothermal energy
production
environmental

description: One major topic addressed by a number of papers is the characterization of Large Magnitude Events (LME), coupled with the investigation of strategies to describe, and ideally mitigate the seismic risk of stimulation operations. Microearthquakes are induced during high-pressure stimulation and they enhance and/or create the required permeability. At the same time, it is crucial not to induce or trigger LME, which may not only cause damage at the surface, but also lower the efficiency of the geothermal system by the creation of “hyper-permeable” pathways. If a LME occurs, it may create a master pathway for fluids that can shortcut the reservoir, preventing heat exchange to be efficient. A better physical understanding of the reservoir- and therefore seismicity generation process is needed in order to develop techniques to reduce the probability of large magnitude earthquakes.

It was first proposed by McGarr (1976) that the total moment of an induced seismicity cloud is proportional to the volume of injected fluid. However, the actual amount of seismicity (or the proportionality factor) can vary quite drastically between different locations. Using a completely different approach of pressure diffusion theory, Shapiro et al. (2007, 2010) arrive at a similar result in which the total number of induced events is proportional to the injected fluid volume. In addition, they describe the proportionality factor, called the seismogenic index, as a function of measurable seismological quantities and rock properties. Combining these considerations with the assumption that seismicity always follows a Gutenberg-Richtertype magnitude distribution leads to a probabilistic description of the maximum expectable magnitude.

The consequence is that the seismic hazard and hence the probability of LME generally increases with injected volume, even though there are significant regional differences. For this reason, we shall differentiate between long-term injection operations (e.g., wastewater or CO2 disposal) where large volumes may accumulate over time, and the rather short term stimulation operations at the beginning of an EGS project. Most of the LME reported in the literature have occurred either after long-term fluid injection (e.g., Ake et al., 2005, Frohlich et al., 2011), or as a result of reservoir impoundment (see Gupta, 2002 for an overview), which can have a similar effect as water injection on reducing criticality in the shallow crust.

Of the short-term stimulation activities, EGS stimulations have generally shown a much higher propensity to produce LME, compared, e.g., to hydraulic fracturing in the oil- and gas industry. Shapiro et al., 2010 find significant higher seismogenic indices for geothermal stimulations in crystalline rocks than for comparable operations in sedimentary formations.

The above considerations and results provide the background for the research results presented in this special issue. Most of this work was conducted as part of the GEISER program, funded by the European Commission within the 7th Framework Programme. The compilation of papers range from the description of large-magnitude seismicity in past geothermal operations to numerical forward-modeling of seismicity clouds, all with the aim to obtain some general conclusions about the creation of LME in geothermal operations.

Evans et al (2012) summarize the results from over 40 European case histories, describing the seismogenic response of crystalline and sedimentary rocks to fluid injection. The data suggest that injection into sedimentary rocks tends to be less seismogenic than in crystalline rocks. Large or damaging earthquakes tend to occur on developed or active fault systems. In other words, large earthquakes are unlikely to occur where there is not a fault large enough to release sufficient energy. Therefore, the risk of producing LME is increased if faults near the well are present. The authors compare the maximum magnitude of each project with the longterm probability of exceeding a threshold peak ground acceleration (PGA) level, and speculate that fluid injection in areas with lower natural seismicity may have a lower risk of producing LME. However, a causal relationship, if any, between the two properties remains unclear and requires a more detailed geomechanical examination of each project.

Table 1.1 lists the cases with the largest LME in order of decreasing event magnitude. Some of these case studies, mainly Basel (Switzerland), Soultz-sous-Forets (France), and Berlin (El Salvador), were studied in detail as part of the on-going research programme. Apart from accurate magnitudes (moment magnitudes wherever available), table 1 also lists the time and location of recorded LME at these sites. It has been frequently observed that LME occur after shut-in and at larger distances to the injection point (i.e. at the edges of the seismic cloud).

Kwiatek et al. (2012, this issue) contribute a case study on the geothermal project in Berlin, El Salvador, where a Mw=3.7 LME occurred 2 weeks after shut-in of the injection. Kwiatek et al. (2012) find that the event is located on some part of an active fault that did not rupture before. They also find a dependence of stress drop to distance from the injection point, similar to what was found by Goertz-Allmann et al. (2011) for the Basel dataset. However, their limited dataset does not allow a spatial mapping of stress drop nor a correlation to the LME.

One of the most intensively studied EGS stimulation that triggered several LME causing damage at the surface is the project in Basel, Switzerland. In Basel the largest magnitude event (ML=3.4) occurred a few hours after shut-in and three additional events with ML&gt;3 occurred one to two month later. These events occurred despite the implementation of a traffic-light system that reduced the wellhead pressure after the first occurrence of a M2.5 event. The largest earthquake occurred at the edge of the seismicity cloud. Comparison of the results of high-precision relative locations of hypocenters pertaining to clusters of similar earthquakes and fault plane solutions with the strike of the strongly aligned seismicity cloud by Deichmann (2012, this report) suggests that the strike or dip of the identified faults deviates substantially from this overall orientation. The concept of a single fault zone with a more or less constant orientation is obviously too simplistic. This has important consequences for models of fluid migration during stimulation and thus of the process of permeability enhancement as well as for seismic hazard assessments. Zhao et al. (2012, this issue) contribute a paper in which they analyse the 19 largest events of the sequence using a full-waveform MT inversion. Special emphasis is on the non-doublecouple (non-DC) components of the moment tensor, where they find an increase of the isotropic component for earlier events near the injection point. Their result implies sizeable volume changes caused by large pore pressures at the early times close to the injection.

Spatial variations of source parameters such as stress drop and b-value of the Basel induced seismicity have been estimated by Goertz-Allmann et al. (2011) and Bachmann et al. (2012), respectively. It has been observed that stress drops are greatly reduced and b-values increased in the vicinity of the injection point where pore pressures are highest. LME in Basel are located in areas of higher stress drop and lower b-value. A comparison with estimated pore pressure variations due to fluid injections suggest that both stress drop and b-value are mainly driven by pore pressure variations. This has important implications on the distribution of seismic hazard.

Catalli et al. (2012) analyse the role of Coulomb failure stress variations (ΔCFS) of the Basel seismicity and find that 70% of event locations are consistent with positive ΔCFS. They find that three out of the four largest events in Basel are located in areas of positive ΔCFS. While pore pressure changes are certainly a first-order factor driving the seismicity at close distances to the injection, their results suggest that Coulomb stress variations ΔCFS due to the already induced seismic events increases in importance at later times and at larger distances from the injection point where the pore pressure change is strongly reduced. They conclude that event interaction is a key parameter in assessing seismic hazard of induced seismicity.

Apart from observational papers, some modeling papers provide more insight into the physical mechanisms driving induced seismicity and the creation of LME. Goertz-Allmann and Wiemer (2012) developed a simple stochastic seed model to simulate induced seismicity in the Basel reservoir using linear pressure diffusion and seismicity triggering based on Coulomb friction. They link Gutenberg’s b-value to differential stress in an inverse linear relationship. By randomly assigning magnitudes based on varying frequency-magnitude distributions, they can estimate the spatio-temporal variation of the probability of an event of a certain magnitude. Goertz-Allmann and Wiemer (2012) find a substantial increase of the mean probability for the time period after shut-in and also at further distances from the injection point, compared to a constant b-value model. Their results not only explain the observations at Basel very well (Goertz-Allmann et al., 2011; Bachmann et al., 2012; Zhao et al., 2012, this issue), but are also consistent with observations at other geothermal projects (Kwiatek et al., 2012, this issue; Charlety et al., 2007; Evans et al., 2012). The presented modeling and magnitude probability estimation technique could be used in a refined traffic light system, where the future magnitude probability is estimated based on the previously recorded seismicity of an on-going operation.

Investigating the influence of some modelling parameters on the resulting seismicity suggests that the probability of LME is reduced if we either drill less deep, or drill into softer formations such as sediments instead of granite. This thought is taken further by Gischig et al. (2012) who investigate the influence of the injection parameters on the probability of occurrence of LME. They show that varying the injected volume has a strong effect on the probability of LME, independently confirming the initial McGarr-Shapiro model of induced seismicity. However, their modelling results also suggest that the injection rate has a strong influence on the magnitude distribution: according to their model, while maintaining the same volume, reducing the wellhead pressure and increasing the injection time reduces the probability of inducing a LME.

One of the consequences of the spatial b-value dependence (Bachmann et al., 2012) and its connection to differential stress (Goertz-Allmann and Wiemer, 2012) is that, due to decreasing b-values, the seismic hazard increases with time and distance from the injection. This could result in a symmetry break of frequency-magnitude distributions with effectively lower b-values (closer to the tectonic average of 1.0) at higher magnitudes. This result is in contrast to calculations by Shapiro et al., 2011 who find a non-linear frequency-magnitude distribution of induced events based on geometrical considerations. The probability of LME is reduced in this model due to the fact that the stimulation volume is finite. The latter model does not take into account the possibility of pre-existing faults that extend beyond the seismogenic volume and might be reactivated over a surface extending beyond the stimulated volume. On the other hand, assuming a power-law frequency-magnitude distribution (constant b-value) implies a power-law distribution of fault sizes in the volume surrounding the injection point. An initial hazard assessment, especially the probability estimation of LME, may require an analysis of the size and distribution of pre-existing faults in the prospect area.
description: FP7