Updated U.S. Geothermal Supply Characterization

This paper documents the approach taken to characterize and represent an updated assessment of U.S. geothermal supply for use in forecasting the penetration of geothermal electrical generation in the National Energy Modeling System (NEMS). This work is motivated by several factors: The supply characterization used as the basis of several recent U.S. Department of Energy (DOE) forecasts of geothermal capacity is outdated; additional geothermal resource assessments have been published; and a new costing tool that incorporates current technology, engineering practices, and associated costs has been released

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey\u27s 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score \u3c 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret

Final Report and Strategic Plan on the Feasibility Study to Assess Geothermal Potential on Warm Springs Reservation Lands.

In 2005 the Confederated Tribes of Warm Springs Tribal Council authorized an evaluation of the geothermal development potential on the Confederated Tribes of Warm Springs Reservation of Oregon. Warm Springs Power & Water Enterprises obtained a grant from the U.S. Department of Energy to conduct a geological assessment and development estimate. Warm Springs Power & Water Enterprises utilized a team of expert consultants to conduct the study and develop a strategic plan. The resource assessment work was completed in 2006 by GeothermEx Inc., a consulting company specializing in geothermal resource assessments worldwide. The GeothermEx report indicates there is a 90% probability that a commercial geothermal resource exists on tribal lands in the Mt. Jefferson area. The geothermal resource assessment and other cost, risk and constraints information has been incorporated into the strategic plan

Defining best practices in the management of geothermal exploration data

International audienceThe objective of this work is to define best practices in the management of geothermal exploration data. This study builds on a questionnaire to survey the geothermal data management practices in mature geothermal markets. The inquiry targeted public Regulatory entities with overview of geothermal resources as well as public and private developers. Topics covered in the questionnaire range from the country status to the database set up. The questionnaire focused on the specifications, usage and investments required for installing/maintaining information systems capable of managing exploration data. In addition, information on the different regulatory frameworks and company policies for managing/sharing exploration data has been gathered to identify the requirements imposed on the design of information systems. The responses were analyzed to identify commonalities in data management practices. They reveal that installing an Information System (IS) is the best practice to systematically and securely manage exploration data. They also provide recommendations with respect to the regulatory framework, data types, data collection methodologies, data storage, data quality control, data accessibility and dissemination, IS architecture, financial investments and human resources required to develop a state-of-the art IS. These results will guide the design of future technical assistance programs for beneficiaries of World Bank support to geothermal exploration activities and it is our belief that they will be beneficial for the geothermal sector at large

Thermal-Plume fibre Optic Tracking (T-POT) test for flow velocity measurement in groundwater boreholes

International audienceWe develop an approach for measuring in-well fluid velocities using point electrical heating combined with spatially and temporally continuous temperature monitoring using Distributed Temperature Sensing (DTS). The method uses a point heater to warm a discrete volume of water. The rate of advection of this plume, once the heating is stopped, equates to the average flow velocity in the well. We conducted Thermal-Plume fibre Optic Tracking (T-POT) tests in a borehole in a fractured rock aquifer with the heater at the same depth and multiple pumping rates. Tracking of the thermal plume peak allowed the spatially varying velocity to be estimated up to 50 m downstream from the heating point, depending on the pumping rate. The T-POT technique can be used to estimate the velocity throughout long intervals provided that thermal dilution due to inflows, dispersion, or cooling by conduction do not render the thermal pulse unresolvable with DTS. A complete flow log may be obtained by deploying the heater at multiple depths, or with multiple point heaters

Geophysics

"This review consisted of consultation of most of the original geophysical reports from the files of GeothermEx, followed by the examination of ENEL's evaluation of existing data for the KERZ. Homogeneous coverage of the KERZ is afforded by only three kinds of geophysical data: passive seismic, aeromagnetic, and airborne EM (VLF). Other types of data, including ground-based geoelectrical , gravimetric, microearthquake, and ground noise, have been collected rather intensively in the Lower KERZ, east of Pahoa; however, these data are virtually non-existent for the Middle and Upper parts of the KERZ. Even within the Lower KERZ, however, the distribution of observing points has been very uneven; station positions have apparently been confined to the irregular, mostly sparse, distribution of roads.

ANALISIS KONDISI RESERVOIR PANAS BUMI DENGAN MENGGUNAKAN DATA GEOKIMIA DAN MONITORING PRODUKSI SUMUR ELS-02 LAPANGAN ELSA

A good reservoir management is needed to maintain the availability and quality of geothermal production fluid. When producing geothermal fluids, there are some changes in reservoir parameters such as declining of reservoir pressure and temperature, chemical composition of geothermal fluids, and states of fluid that would affect the quality of reservoir by mixing, boiling, or cooling processes that may be happened because of those changes. It is becoming a concern on reservoir management. In this case, chemical concentrations of fluids monitoring is one of methods that can perform to reach a well reservoir management of geothermal field. With chemical monitoring process, current reservoir condition and processes that occurred during exploitation can be defined. In ELS-02 by monitoring and analyzing its enthalpy changes, chloride concentration changes, and NCG concentration changes and supported by its calcium, sulphate, and carbonate concentration profile, two processes could be defined: mixing with surface cooler water and reinjection breakthrough. Other than that, casing leak that causing surface water enter the well could be detected.  These become a sign to reservoir engineer to prepare for problems that may occur in near time term relating to well problem such as scaling and long time problem like massive cooling or drying of reservoir. After all, further development scenario of Elsa field can be made to improve its performance in producing fluids and heats.

Underground thermal energy storage in subarctic climates: A feasibility study conducted in Kuujjuaq (QC, Canada)

Underground thermal energy storage can provide space and water heating and has been used in temperate climates so far. A step forward is to evaluate the efficiency and viability in arctic to subarctic environments, where rather low ground and air temperatures can make the design of such systems difficult. The present contribution describes the design of an underground storage system in Kuujjuaq (Québec, Canada) to heat the drinking water distributed in the town. The system was designed and modeled with TRNSYS and a parametric study was carried out to improve its efficiency based on 5-year simulations. The 20% of the 425 MWh annual demand can be satisfied by a solar collector area of 500 m2 coupled to a 10,000 m3 underground storage through two short term tanks. Further improvements could be adopted to reach the target of 50% energy from the underground store

Thermal-plume fibre optic tracking (T-POT) test for flow velocity measurement in groundwater boreholes

We develop an approach for measuring in-well fluid velocities using point electrical heating combined with spatially and temporally continuous temperature monitoring using distributed temperature sensing (DTS). The method uses a point heater to warm a discrete volume of water. The rate of advection of this plume, once the heating is stopped, equates to the average flow velocity in the well. We conducted thermal-plume fibre optic tracking (T-POT) tests in a borehole in a fractured rock aquifer with the heater at the same depth and multiple pumping rates. Tracking of the thermal plume peak allowed the spatially varying velocity to be estimated up to 50 m downstream from the heating point, depending on the pumping rate. The T-POT technique can be used to estimate the velocity throughout long intervals provided that thermal dilution due to inflows, dispersion, or cooling by conduction does not render the thermal pulse unresolvable with DTS. A complete flow log may be obtained by deploying the heater at multiple depths, or with multiple point heaters