Characterization of geothermal reservoirs’ parameters by inverse problem resolution and geostatistical simulations

BTES (borehole thermal energy storage)systems exchange thermal energy by conduction with the surrounding ground through borehole materials. The spatial variability of the geological properties and the space-time variability of hydrogeological conditions affect the real power rate of heat exchangers and, consequently, the amount of energy extracted from / injected into the ground. For this reason, it is not an easy task to identify the underground thermal properties to use when designing. At the current state of technology, Thermal Response Test (TRT) is the in situ test for the characterization of ground thermal properties with the higher degree of accuracy, but it doesn’t fully solve the problem of characterizing the thermal properties of a shallow geothermal reservoir, simply because it characterizes only the neighborhood of the heat exchanger at hand and only for the test duration. Different analytical and numerical models exist for the characterization of shallow geothermal reservoir, but they are still inadequate and not exhaustive: more sophisticated models must be taken into account and a geostatistical approach is needed to tackle natural variability and estimates uncertainty. The approach adopted for reservoir characterization is the “inverse problem”, typical of oil&gas field analysis. Similarly, we create different realizations of thermal properties by direct sequential simulation and we find the best one fitting real production data (fluid temperature along time). The software used to develop heat production simulation is FEFLOW 5.4 (Finite Element subsurface FLOW system). A geostatistical reservoir model has been set up based on literature thermal properties data and spatial variability hypotheses, and a real TRT has been tested. Then we analyzed and used as well two other codes (SA-Geotherm and FV-Geotherm) which are two implementation of the same numerical model of FEFLOW (Al-Khoury model).

Continuous geochemical monitoring by mass-spectometer in the Campi Flegrei geothermal area. An application at Pisciarelli-Solfatara (diffuse and fumarolic gases) and at the mud gases during drilling of the CFDDP pilot hole

During this work has been developed an innovative methodology for continuous and in situ gas monitoring (24/24 h) of fumarolic and soil diffusive emissions applied to the geothermal and volcanic area of Pisciarelli near Agnano inside the Campi Flegrei caldera (CFc). In literature there are only scattered and in discrete data of the geochemical gas composition of fumarole at Campi Flegrei; it is only since the early ’80 that exist a systematic record of fumaroles with discrete sampling at Solfatara (Bocca Grande and Bocca Nuova fumaroles) and since 1999, even at the degassing areas of Pisciarelli. This type of sampling has resulted in a time series of geochemical analysis with discontinuous periods of time set (in average 2-3 measurements per month) completely inadequate for the purposes of Civil Defence in such high volcanic risk and densely populated areas. For this purpose, and to remedy this lack of data, during this study was introduced a new methodology of continuous and in situ sampling able to continuously detect data related and from its soil diffusive degassing. Due to its high sampling density (about one measurement per minute therefore producing 1440 data daily) and numerous species detected (CO2, Ar, 36Ar, CH4, He, H2S, N2, O2) allowing a good statistic record and the reconstruction of the gas composition evolution of the investigated area. This methodology is based on continuous sampling of fumaroles gases and soil degassing using an extraction line, which after undergoing a series of condensation processes of the water vapour content - better described hereinafter - is analyzed through using a quadrupole mass spectrometer

Secondary Microseisms Characterization and Green’s Function Extraction at the Larderello-Travale Geothermal Field (Italy)

Over the past ten years, the cross-correlation of long-time series of ambient seismic noise (ASN) has been widely adopted to extract the surface-wave part of the Green’s Functions (GF). This stochastic procedure relies on the assumption that ASN wave-field is diffuse and stationary. At frequencies <1Hz, the ASN is mainly composed by surface-waves, whose origin is attributed to the sea-wave climate. Consequently, marked directional properties may be observed, which call for accurate investigation about location and temporal evolution of the ASN-sources before attempting any GF retrieval. Within this general context, this thesis is aimed at a thorough investigation about feasibility and robustness of the noise-based methods toward the imaging of complex geological structures at the local (∼10-50km) scale. The study focused on the analysis of an extended (11 months) seismological data set collected at the Larderello-Travale geothermal field (Italy), an area for which the underground geological structures are well-constrained thanks to decades of geothermal exploration. Focusing on the secondary microseism band (SM;f>0.1Hz), I first investigate the spectral features and the kinematic properties of the noise wavefield using beamforming analysis, highlighting a marked variability with time and frequency. For the 0.1-0.3Hz frequency band and during Spring- Summer-time, the SMs waves propagate with high apparent velocities and from well-defined directions, likely associated with ocean-storms in the south- ern hemisphere. Conversely, at frequencies >0.3Hz the distribution of back- azimuths is more scattered, thus indicating that this frequency-band is the most appropriate for the application of stochastic techniques. For this latter frequency interval, I tested two correlation-based methods, acting in the time (NCF) and frequency (modified-SPAC) domains, respectively yielding esti- mates of the group- and phase-velocity dispersions. Velocity data provided by the two methods are markedly discordant; comparison with independent geological and geophysical constraints suggests that NCF results are more robust and reliable.