Utilizing supercritical geothermal systems: a review of past ventures and ongoing research activities
Supercritical geothermal systems are very high-temperature geothermal systems that are located at depths near or below the brittle–ductile transition zone in the crust where the reservoir fluid is assumed to be in the supercritical state, that is for pure water, temperature and pressure are, respectively, in excess of 374 °C and 221 bar. These systems have garnered attention in recent years as a possible type of unconventional geothermal resource due to their very high enthalpy fluids. Supercritical conditions are often found at the roots of volcanic-hosted hydrothermal systems. More than 25 deep wells drilled in geothermal fields such as The Geysers, Salton Sea, and on Hawaii (USA), Kakkonda (Japan), Larderello (Italy), Krafla (Iceland), Los Humeros (Mexico), and Menengai (Kenya) have encountered temperatures in excess of 374 °C, and in some cases have encountered magma. Although fluid entries were documented for some of these wells, it remains an open question if permeability can be maintained at high enthalpy conditions. The IDDP-1 well at Krafla encountered magma, and ended up producing very high enthalpy fluids; however, these fluids were very corrosive and abrasive. Innovative drilling and well completion techniques are therefore needed to deal with the extreme temperatures and aggressive fluid chemistry compositions of these systems. New efforts are underway in Japan (northern Honshu), Italy (Larderello), Iceland (Reykjanes peninsula and Krafla), Mexico (Los Humeros), USA (Newberry), and New Zealand (Taupo Volcanic Zone) to investigate supercritical systems. Here, we review past studies, describe current research efforts, and outline the challenges and potential opportunities that these systems provide for international collaboration to ultimately utilize supercritical geothermal systems as a geothermal energy resource.
Resistivity-Based Temperature Estimation of the Kakkonda Geothermal Field, Japan, Using a Neural Network and Neural Kriging
Neural network has been successfully used for field-scale temperature estimation based on resistivity data. Although the methodology is able to estimate temperature distribution based on geophysical data with a limited number of temperature data, it has also been shown that the estimation accuracy can decrease as distance increases from temperature measurements. In this letter, we developed a resistivity-based neural kriging approach to improve the accuracy of estimated temperatures. The neural kriging method in this letter incorporated a variogram of temperature data, which constrained the spatial distribution of temperature. We examined the accuracy of standard neural network approaches, as well as the neural kriging approach, using a cross-validation test. The examination showed that when neural kriging was used, the estimation error and variogram error were reduced by 6%–87% and 191%–285%, respectively. The improvement was also significant at areas distant from well logs. The pattern of estimated temperatures matched the underlying geology and was in accordance with previous studies. The proposed methodology can incorporate other types of geophysical data for estimating other physical parameter distributions.
Time and Heritage
As a historian studying what can be considered a form of intellectual history, I have slowly come to adopt Michel de Certeau’s observation made at the end of the 1980s that ‘objectifying the past, for the last three centuries, has undoubtedly left unconsidered time within a discipline that has continued to use it as a taxonomic instrument’. To a certain degree, time has become commonplace for the historian who has preserved or instrumentalized it. It is not considered because it is inconceivable, but because we do not think of it or, more simply, we do not think about it. As a historian attempting to pay attention to the time I’m living in, I have thus, like many others, noticed the swift development of the category of the present until it has become obvious that the present is omnipresent. This is what I refer to here as ‘presentism’.