Stanford Geothermal WorkshopFebruary 9-11, 2026

Geothermal resource exploration is a key pathway to harnessing renewable energy from the Earth’s heat, offering a sustainable alternative to conventional energy sources as part of the reliable baseload. Enhanced Geothermal Systems (EGS) introduce engineering techniques to improve subsurface reservoirs and extract significant energy from hot, low-permeability rock where traditional systems cannot achieve sufficient performance. By drilling into hot rock and injecting fluids, engineering induced fracture networks that allow working fluids to circulate, transfer heat, and return to the surface to produce energy for increasing demands. Alongside multi-level dataset integration, understanding the geometry and evolution of these fracture and fault networks is critical to optimizing fluid flow, heat recovery, and reservoir sustainability. This work introduces FLOWDASH, an integrated software platform designed to visualize such datasets and analyze fracture networks and associated performance metrics at multiple scales—well-level, segment-level, and stage-level. FlowDash integrates completion and stimulation data, geophysical observations, machine- learning techniques, and economic analysis to support EGS decision-making in an interactive fashion. The platform’s visualization module is designed to present multi-scale reservoir features using interactive well-path schematics, stage-by-stage fracture mapping, temporal seismic event plots, and heat-flow trends. These graphical interfaces allow users to overlay production, seismic, and cost data for rapid interpretation. The software architecture uses a Python-based back end for data handling and computations, coupled with a Tkinter-driven front end that supports real-time updates of charts and diagrams. FlowDash includes a built-in cost- analysis tool that allows users to calculate and compare energy production potential, projected profit, as well as generate visual graphs from these calculations—which users can download such plots directly in various of formats such as images to create customized reports. To characterize the subsurface, the platform processes passive seismic monitoring data and employs up to five unsupervised machine-learning algorithms for micro seismic clustering, b- value quantification, and hydraulic diffusivity analysis. Both hydraulic and natural fractures are delineated to capture reservoir complexity, and the integrated workflow bridges technical teams and decision-makers by combining geophysical interpretation with economic projections. The platform provides an intuitive and centralized environment for interpreting complex datasets, analyses, and results of fracture and fault systems that emerge during EGS operations. The tool enhances reservoir characterization by correlating seismic-derived fracture geometry with production metrics and thermal performance. Early applications of FlowDash have demonstrated its potential to identify high-permeability zones, optimize stimulation designs, improve reservoir-pressure management, and guide decisions on interference- mitigation and re-stimulation planning. FlowDash streamlines workflows for both technical and financial assessments, supporting data-driven strategies for improving reservoir sustainability and energy output. FlowDash unifies geophysical, machine-learning, and economic analyses within a single, intuitive platform to improve geothermal reservoir management. This integrated approach equips engineers, researchers, and stakeholders with actionable insights that enhance efficiency, reliability, and sustainability in geothermal energy production. More datasets and analysis for EGS are still on-going for further insights to support decision making.

Topic: Enhanced Geothermal Systems