There is growing concern about seismicity triggered by human activities, whereby small increases in stress bring tectonically loaded faults to failure. Examples of such activities include mining, impoundment of water, stimulation of geothermal fields, extraction of hydrocarbons and water, and the injection of water, CO2 and methane into subsurface reservoirs1. In the absence of sufficient information to understand and control the processes that trigger earthquakes, authorities have set up empirical regulatory monitoring-based frameworks with varying degrees of success2,3. Field experiments in the early 1970s at the Rangely, Colorado (USA) oil field4 suggested that seismicity might be turned on or off by cycling subsurface fluid pressure above or below a threshold. Here we report the development, testing and implementation of a multidisciplinary methodology for managing triggered seismicity using comprehensive and detailed information about the subsurface to calibrate geomechanical and earthquake source physics models. We then validate these models by comparing their predictions to subsequent observations made after calibration. We use our approach in the Val d’Agri oil field in seismically active southern Italy, demonstrating the successful management of triggered seismicity using a process-based method applied to a producing hydrocarbon field. Applying our approach elsewhere could help to manage and mitigate triggered seismicity.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Relevant data are available at https://doi.org/10.6084/m9.figshare.c.5401509 including the computational meshes with embedded stratigraphic horizons and fault surfaces for the regional and local models, pressure history on the CMF, input file and script for the seismicity rate model, and source data for Figs. 1c and 3. Total field monthly oil and gas production as tabulated by the Italian Ministry of Economic Development are included in the figshare repository. Some input data for the flow–geomechanics models contain proprietary information, made available by Eni for the current study under confidentiality agreement. These data are available from S.M. (firstname.lastname@example.org) with permission of Eni. Source data are provided with this paper.
The seismicity rate modelling code is included at https://doi.org/10.6084/m9.figshare.14519028.
Foulger, G. R., Wilson, M., Gluyas, J., Julian, B. R. & Davies, R. Global review of human-induced earthquakes. Earth Sci. Rev. 178, 438–514 (2018).
Baisch, S., Koch, C. & Muntendam-Bos, A. Traffic light systems: to what extent can induced seismicity be controlled? Seismol. Res. Lett. 90, 1145–1154 (2019).
Kwiatek, G. et al. Controlling fluid-induced seismicity during a 6.1-km-deep geothermal stimulation in Finland. Sci. Adv. 5, eaav7224 (2019).
Raleigh, C. B., Healy, J. H. & Bredehoeft, J. D. An experiment in earthquake control at Rangely, Colorado. Science 191, 1230–1237 (1976).
Improta, L. et al. Reservoir structure and wastewater induced seismicity at the Val d’Agri oilfield (Italy) shown by three-dimensional Vp and Vp/Vs local earthquakes tomography. J. Geophys. Res. Solid Earth 122, 9050–9082 (2017).
Buttinelli, M., Improta, L., Bagh, S. & Chiarabba, C. Inversion of inherited thrusts by wastewater injection induced seismicity at the Val d’Agri oilfield (Italy). Sci. Rep. 6, 37165 (2016).
Cello, G. & Mazzoli, S. Apennine tectonics in southern Italy: a review. J. Geodyn. 27, 191–211 (1998).
D’Argenio, B., Pescatore, T. & Scandone, P. Structural pattern of the Campania-Lucania Apennines. Quad. Ric. Sci. 90, 313–327 (1975).
Butler, R. W. H. et al. in Thrust Tectonics and Hydrocarbon Systems: American Association of Petroleum Geologists Memoir 82 (ed. McClay, K. R.) 647–667 (American Association of Petroleum Geologists, 2004).
Valoroso, L. Upper Crustal Structure and Seismotectonics of the Val d’Agri Area, Southern Italy, Through Integration of Local Earthquake and Active Seismic Tomographies, and Geological Mapping. PhD Thesis, Università degli Studi di Napoli Federico II (2007).
Mazzoli, S. et al. Reconstruction of continental margin architecture deformed by the contraction of the Lagonegro Basin, southern Italy. J. Geol. Soc. Lond. 158, 309–319 (2001).
Shiner, P., Beccaccini, A. & Mazzoli, S. Thin-skinned versus thick-skinned structural models for Apulian carbonate reservoirs: constrains from the Val d’Agri Fields, S Apennines, Italy. Mar. Pet. Geol. 21, 805–827 (2004).
Malinverno, A. & Ryan, W. B. F. Extension in the Tyrrhenian sea and shortening in the Apennines as a result of arc migration driven by sinking of the lithosphere. Tectonics 5, 227–245 (1986).
Dewey, J. F., Helman, M. L., Turco, E., Hutton, D. W. H. & Knott, S. P. in Alpine Tectonics (eds Coward, M. P., Dietrich, D. & Park, R. G.) Geol. Soc. Special Publication no. 45, 265–283 (Blackwell Scientific, 1989).
Royden, L. H., Patacca, E. & Scandone, P. Segmentation and configuration of subducted lithosphere in Italy: an important control on thrust-belt and foredeep-basin evolution. Geology 15, 714–717 (1987).
Maschio, L., Ferranti, L. & Burrato, P. Active extension in Val d’Agri area, Southern Apennines, Italy: implications for the geometry of seismogenic belt. Geophys. J. Int. 162, 591–609 (2005).
Devoti, R., Esposito, A., Pietrantonio, G., Pisani, A. R. & Riguzzi, F. Evidence of large scale deformation patterns from GPS data in the Italian subduction boundary. Earth Planet. Sci. Lett. 311, 230–241 (2011).
Silverii, F., D’Agostino, N., Métois, M., Fiorillo, F. & Ventafridda, G. Transient deformation of karst aquifers due to seasonal and multi-year groundwater variations observed by GPS in Southern Apennines (Italy). J. Geophys. Res. Solid Earth 121, 8315–8337 (2016).
Albarello, D., Camassi, R. & Rebez, A. Detection of space and time heterogeneity in the completeness level of a seismic catalogue by a robust statistical approach: an application to the Italian area. Bull. Seismol. Soc. Am. 91, 1694–1703 (2001).
Stucci, M., Albini, P., Mirto, C. & Rebez, A. Assessing the completeness of Italian historical earthquake data. Ann. Geophys. 47, 659–673 (2004).
Rovida, A. et al. The Italian earthquake catalogue CPTI15. Bull. Earthquake Eng. 18, 2953–2984 (2020).
Valoroso, L. et al. Active faults and induced seismicity in the Val d’Agri area (Southern Apennines, Italy). Geophys. J. Int. 178, 488–502 (2009).
Telesca, L., Giocoli, A., Lapenna, V. & Stabile, T. A. Robust identification of periodic behavior in the time dynamics of short seismic series: the case of seismicity induced by Pertusillo Lake, southern Italy. Stochastic Environ. Res. Risk Assess. 29, 1437–1446 (2015).
Plesch, A. et al. Community fault model (CFM) for southern California. Bull. Seismol. Soc. Am. 97, 1793–1802 (2007).
Biot, M. A. General theory of three-dimensional consolidation. J. Appl. Phys. 12, 155–164 (1941).
Coussy, O. Mechanics of Porous Continua (Wiley, 1995).
Freed, A. Earthquake triggering by static, dynamic, and postseismic stress transfer. Annu. Rev. Earth Planet. Sci. 33, 335–367 (2005).
Hardebeck, J. L., Nazareth, J. J. & Hauksson, E. The static stress change triggering model: constraints from two southern California aftershock sequences. J. Geophys. Res. Solid Earth 103 (B10), 24427–24437 (1998).
Aki, K. Generation and propagation of G waves from the Niigata earthquake of June 16, 1964. Part 2. Estimation of earthquake moment, released energy, and stress-strain drop from the G wave spectrum. Bull. Earthq. Res. Inst. 44, 73–78 (1966).
Hanks, T. C. & Kanamori, H. A moment magnitude scale. J. Geophys. Res. Solid Earth 84 (B5), 2348–2350 (1979).
Marone, C. Laboratory-derived friction laws and their application to seismic faulting. Annu. Rev. Earth Planet. Sci. 26, 643–696 (1998).
Linker, M. F. & Dieterich, J. H. Effects of variable normal stress on rock friction: observations and constitutive equations. J. Geophys. Res. Solid Earth 97 (B4), 4923–4940 (1992).
Dieterich, J. H. Earthquake nucleation on faults with rate- and state-dependent friction. Tectonophysics 211, 115–134 (1992).
Dieterich, J. H. A constitutive law for rate of earthquake production and its application to earthquake clustering. J. Geophys. Res. Solid Earth 99, 2601–2618 (1994).
Kaiser, J. Kenntnisse und Folgerungen aus der Messung von Geräuschen bei Zugbeanspruchung von metallischen Werkstoffen. Arch. Für Isenhütten-Wesen 24, 43–45 (1953).
Alghannam, M. & Juanes, R. Understanding rate effects in injection-induced earthquakes. Nat. Commun. 11, 3053 (2020).
DISS Working Group. Database of Individual Seismogenic Sources (DISS), Version 3.2.1: A compilation of potential sources for earthquakes larger than M 5.5 in Italy and surrounding areas (Istituto Nazionale di Geofisica e Vulcanologia, accessed 2 January 2019); http://diss.rm.ingv.it/diss/
Stabile, T. A. et al. Relationship between seismicity and water level of the Pertusillo reservoir (southern Italy). Boll. Geofis. Teor. Appl. 56, 505–517 (2015).
Ferranti, L., Maschio, L. & Burrato, P. Field Trip Guide to Active Tectonics Studies in the High Agri Valley. http://hdl.handle.net/2122/2749 (Istituto Nazionale di Geofisica e Vulcanologia, 2007).
Stabile, T. A. et al. Evidences of low-magnitude continued reservoir induced seismicity associated with the Pertusillo artificial lake (southern Italy). Bull. Seismol. Soc. Am. 104, 1820–1828 (2014).
Aziz, K. & Settari, A. Petroleum Reservoir Simulation (Applied Science, 1979).
INTERSECT User Guide/Technical Description v.2016.2 (Schlumberger, 2016).
Aagaard, B. T., Knepley, M. G. & Williams, C. A. A domain decomposition approach to implementing fault slip in finite-element models of quasi-static and dynamic crustal deformation. J. Geophys. Res. Solid Earth 118, 3059–3079 (2013).
ABAQUS User Manual v.2019 (Dassault Systèmes, 2017).
Xu, Y., Cavalcante Filho, J. S., Yu, W. & Sepehrnoori, K. Discrete-fracture modeling of complex hydraulic-fracture geometries in reservoir simulators. SPE Reservoir Eval. Eng. 20, 403–422 (2017).
Cosentino, L. Integrated Reservoir Studies (Editions Technip, 2001).
Cucci, L., Pondrelli, S., Frepoli, A., Mariucci, M. T. & Moro, M. Local pattern of stress field and seismogenic sources in Melandro Pergola basin and in Agri valley (Southern Italy). Geophys. J. Int. 156, 575–583 (2004).
Della Vecchia, G., Pandolfi, A., Musso, G. & Capasso, G. An analytical expression for the determination of in situ stress state from borehole data accounting for breakout size. Int. J. Rock Mech. Min. Sci. 66, 64–68 (2014).
Chiarabba, C., Jovane, L. & Di Stefano, R. A new view of Italian seismicity using 20 years of instrumental recordings. Tectonophys. 395, 251–268 (2005).
Jha, B. & Juanes, R. Coupled multiphase flow and poromechanics: A computational model of pore pressure effects on fault slip and earthquake triggering. Wat. Resour. Res. 50, 3776–3808 (2014).
Dieterich, J., Cayol, V. & Okubo, P. The use of earthquake rate changes as a stress meter at Kilauea volcano. Nature 408, 457–460 (2000).
Toda, S., Stein, R. S. & Sagiya, T. Evidence from the ad 2000 Izu islands earthquake swarm that stressing rate governs seismicity. Nature 419, 58–61 (2002).
Kroll, K. A., Richards‐Dinger, K. B., Dieterich, J. H. & Cochran, E. S. Delayed seismicity rate changes controlled by static stress transfer. J. Geophys. Res. Solid Earth 122, 7951–7965 (2017).
Helmstetter, A. & Shaw, B. E. Relation between stress heterogeneity and aftershock rate in the rate-and-state model. J. Geophys. Res. 111, B07304 (2006).
Cochran, E. S., Vidale, J. E. & Tanaka, S. Earth tides can trigger shallow thrust fault earthquakes. Science 306, 1164–1166 (2004).
Parsons, T., Toda, S., Stein, R. S., Barka, A. & Dieterich, J. H. Heightened odds of large earthquakes near Istanbul: an interaction-based probability calculation. Science 288, 661–665 (2000).
Segall, P. & Lu, S. Injection-induced seismicity: poroelastic and earthquake nucleation effects. J. Geophys. Res. 120, 5082–5103 (2015).
We thank L. Improta for discussions and for providing the high-precision Double Difference events catalogue in Fig. 1; T. Stabile for discussions; M. Mileti for support; G. Roncari, L. Barzaghi, F. Ferulano and A. Orefice for contributions to GPS and seismic data collection and processing; B. Jha for early contributions to the regional geomechanical modelling, including mesh generation; and D. Susanni and L. Magagnini for assistance in obtaining information and organization. We thank Eni and Shell for the authorization to publish this paper and share the data; M. van der Baan and D. Eaton for suggestions that benefitted the manuscript; and A. Puliti for making this study possible.
Eni initiated a research project to provide an independent assessment of triggered seismicity at the Val d’Agri field based on the most advanced available scientific and technical knowledge. For this purpose, Eni contracted Ramboll Italy S.r.l. to hire the consulting team of J.D., C.F., B.H.H., R.J., A.P. and J.H.S. A.C., S.M., M.M., M. Mileti and L.O. were Eni project references. Eni provided computing resources and technical assistance. The research consulting team submitted a report to Eni addressing activity until the end of 2016. To expand the research scope, Eni’s local model—which was developed in parallel—was embedded into the regional model. It was then decided to publish the joint research in the peer-reviewed scientific literature. After their consulting report was completed and presented, the consulting team did not receive further financial support from Eni.
Peer review information Nature thanks David Eaton, Mirko van der Baan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Daily injection rate (black solid line) and microseismicity (red circles), 1–30 June 2006. The right y axis indicates event magnitude (ML)
Computational grid of the regional geomechanical model. Top, view of the tetrahedral grid of the computational domain, of approximate dimensions 80 × 50 × 10 km, with a total of approximately 204,000 cells. Top left, the brown shaded volume indicates the region above the Irpine layers. Top right, a slice through a portion of the grid indicating the grid size within and outside the reservoir. Bottom, view of the gridded faults included in the computational model.
Top, computational mesh of the local reservoir and geomechanical model. The tetrahedral mesh conforms to the structural model including a complete set of faults, and reservoir and overburden layers. For clarity, a subset of the faults is shown with mesh nodes restricted to a portion of the model. The coloured symbols indicate the pre-production vertical effective stress. Bottom, schematic representation of finite element modelling for the CMF showing node pairs that, starting from an initial condition (left) in which the nodes are superimposed (duplicated), may move separately following the fault slip (right). The seismic moment can be computed by integrating the nodal slip on the fault.
Perspective view looking westward of ΔCFF (1993–2016) on all model faults. The colour bar is clipped at a ΔCFF value of ± 0.1 MPa to show details of small regions of destabilizing ΔCFF at shale–carbonate contacts.
Observed cumulative number of earthquakes (EQs) on the CMF over time (red line) compared with the results of three realizations of the rate–state model (black lines). These models all use the same values for α and γ0, but three different pairs of parameters μ and a, providing essentially indistinguishable results. This comparison demonstrates that, although there are large trade-offs among parameters, the resulting forecasts are tightly constrained
Top, historical production and reinjection data for the Val d’Agri field: daily oil (green), gas (red) and water (blue) production data and water reinjection (light blue) in CM2 well. Bottom, shut-in pressure reported at datum-depth (2,400 mTVDssl) for representative wells of Monte Alpi (blue), Monte Enoc (green) and Cerro Falcone (red) culminations.
Locations of the seismic stations operating in the neighbourhood of the Val d’Agri field. For map coordinates, see Fig. 1. The background image is constructed from Copernicus Sentinel data (2017).
Seismic time slice at 2,300 ms from Val d’Agri three-dimensional seismic reflection volume, showing constraints on the F10 fault and the CMF. a, Image showing CM2 well and other reservoir faults (RF) included in the regional model. b, Interpreted seismic reflections (yellow), and F10 and CMF traces. Note that the primary constraint on the CMF location is from seismicity.
Simulated (black curves) and observed (red dots) bottom hole reservoir pressure at representative well locations for the regional model (left) and the local model (right)
Comparison of X (top row), Y (middle row), and Z (bottom row) components of model predictions (lines) and GPS estimates (symbols) of relative displacements between GPS sites SIRI (left column), MTSN (middle column) and MCEL (right column) and reference site PTRP. GPS site locations are shown in Fig. 1. Predictions for our preferred model, with reservoir Biot coefficient 0.1, are given by the black lines; predictions for an alternative model with reservoir Biot coefficient 0.3 are given by the blue lines. Displacements are in the model coordinate system; the lateral displacements are projected along the x axis (positive to the northeast) and along the y axis (positive to the northwest)
About this article
Cite this article
Hager, B.H., Dieterich, J., Frohlich, C. et al. A process-based approach to understanding and managing triggered seismicity. Nature 595, 684–689 (2021). https://doi.org/10.1038/s41586-021-03668-z