Abstract
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.
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Data availability
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. (stefano.mantica@eni.com) with permission of Eni. Source data are provided with this paper.
Code availability
The seismicity rate modelling code is included at https://doi.org/10.6084/m9.figshare.14519028.
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Acknowledgements
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.
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B.H.H., R.J., D.C., J.H.S., A.P., C.F. and J.D. designed and carried out the initial phase of the project (the regional model) with input from A.C., S.M., M.M. and L.O; S.M., F.B., F.C., A.C. and S.P. improved the project by adding the local flow and geomechanical model, including seismic moment calculations. J.H.S. and A.P. developed the regional and local structural representation with input from M.M. R.J. and D.C. conducted the regional flow and geomechanical modelling, in collaboration with A.C., S.M., F.B. and F.C. B.H.H. analysed the geodetic data and led the writing. C.F. analysed the seismicity data. J.D. carried out the seismicity rate calculations. J.D., C.F., R.J., S.M., J.H.S., A.C., L.O. and A.P. discussed the results and participated in writing and reviewing the manuscript.
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Competing interests
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.
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Extended data figures and tables
Extended Data Fig. 1 Comparison of injection rates and earthquakes.
Daily injection rate (black solid line) and microseismicity (red circles), 1–30 June 2006. The right y axis indicates event magnitude (ML)
Extended Data Fig. 2 Computational mesh for structural model of field and regional faults.
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.
Extended Data Fig. 3 Representation of faults in the computational mesh.
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.
Extended Data Fig. 4 CFF values on Val d’Agri faults.
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.
Extended Data Fig. 5 Modelled and observed number of earthquakes on the CMF.
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
Extended Data Fig. 6 Well production, reinjection and pressure data.
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.
Extended Data Fig. 7 Map of seismic stations in Val d’Agri.
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).
Extended Data Fig. 8 Seismic reflection data near CM2 well defining F10 fault.
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.
Extended Data Fig. 9 Modelled and observed reservoir pressures.
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)
Extended Data Fig. 10 Modelled and observed ground displacements.
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)
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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
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DOI: https://doi.org/10.1038/s41586-021-03668-z
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