Büyük, Ersin; Uzel, Bora; Sözbilir, Hasan; Baba, Alper

Electrical resistivity has been shown to offer significant insights into post-seismic changes in geothermal reservoirs; however, its interpretation is complicated by the fact that it is controlled not only by temperature, but also by porosity and clay content. In this study, we used a post-seismic electrical resistivity-depth model from magnetotelluric data to estimate the temperature changes following a seismic swarm in the vicinity of the Tuzla Geothermal Field in the Biga Peninsula of northwestern Anatolia. In the absence of pre-seismic resistivity data for the field, we attribute the characteristic part of the post-seismic model to the seismic swarm. Temperature changes were then evaluated using pre- and post-seismic wellhead temperature observations together with the post-seismic resistivity-depth model. In order to address the relationships between resistivity and petrophysical parameters, the present study combined the established equations for temperature, porosity and clay-related effects. The estimation of petrophysical parameters from combined equations was then conducted using the differential evolution algorithm (DEA), a method that has gained popularity due to its effectiveness in solving non-linear optimization problems. The main contribution of this study lies in the integrated use of a unified petrophysical framework and the DEA to jointly estimate post-seismic changes in temperature, porosity and clay content from the resistivity model. Furthermore, an estimation was made of the reduction in stored heat within the defined reservoir zone. The results indicate that the observed increase in resistivity is primarily associated with a decrease in reservoir temperature, which is consistent with the observed decrease in wellhead temperatures. In addition, changes in clay content and porosity contribute significantly to the resistivity response. The findings demonstrate the efficacy of models of resistivity-depth, obtained following seismic activities, in facilitating the inference of changes in petrophysical parameters and thermal state in geothermal systems.

Büyük, Ersin

Particle swarm optimization, one of the modern global optimization methods, is attracting widespread interest because it overcomes the difficulties of conventional inversion techniques, such as trapping at a local minimum and/or initial model dependence. The main characteristic of particle swarm optimization is the large search space of parameters, which in a sense allows the exploration of the entire objective function space if the input parameters are properly chosen. However, in the case of a high-dimensional model space, the numerical instability of the solution may increase and lead to unrealistic models and misinterpretations due to the sampling problem of particle swarm optimization. Therefore, smoothness-constrained regularization techniques used for the objective function or model reduction techniques are required to stabilize the solution. However, weighting and combining objective function terms is partly a subjective process, as the regularization parameter is generally chosen based on some kind of criteria of how the smoothing constraints affect the data misfits. This means that it cannot be completely predefined but needs to be adjusted during the inversion process, which begins with the response of an initial model. In this paper, a new modelling approach is proposed to obtain a smoothness-constrained model from magnetotelluric data utilizing multi-objective particle swarm optimization based on the Pareto optimality approach without using a regularization parameter and combining several objective function terms. The presented approach was verified on synthetic models and an application with field data set from the Çanakkale–Tuzla geothermal field in Turkey. Findings from these analyses confirm the usefulness of the method as a new approach for all constrained inversions of geophysical data without the need to combine the objective function terms weighted by a regularization parameter.