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Horne, Roland N.

Publications and source records attributed to Horne, Roland N..

13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fracture Characterization Via AI‐Assisted Analysis of Temperature Logs

Abstract Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. A long short‐term memory fully convolutional network (LSTM‐FCN) is used both to capture long‐term dependencies in sequential temperature data and to distill local features around fractures. A wellbore and fractured‐reservoir thermal model is established to generate temperature data for network training. The trained LSTM‐FCN exhibits a unique ability to detect multiple fractures intersecting a borehole. We use the LSTM‐FCN algorithm to evaluate the effectiveness of different‐stage wellbore temperature measurements on fracture detection in a complex fractured system. Our experiments reveal that the use of various‐stage temperature information as an input feature set improves the robustness of fracture detection to noise interference. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost.

Yang, Xiaoyu↗
DOE OSTI2022

The EGS Collab Project – Stimulations at Two Depths

The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. We have completed Experiment 1 (of 3), which examined hydraulic fracturing in a well-characterized underground fractured phyllite test bed at a depth of approximately 1.5 km at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests using ambient temperature and chilled water. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. More detailed numerical simulation was performed to answer key experimental design questions, forecast fracture propagation trajectories and extents, and analyze and evaluate results. Data are freely available from the Geothermal Data Repository. Experiment 2 examines the potential for hydraulic shearing in amphibolite at a depth of about 1.25 km at SURF. This site has a different set of stress and fracture conditions than Experiment 1. The Experiment 2 testbed consists of nine subhorizontal boreholes configured in two fans of two boreholes which surround the testbed and contain grouted-in electrical resistance tomography, seismic sensors, active seismic sources and distributed fiber sensors. A “five-spot” set of test wells that extends from a custom mined alcove includes an injection well and four production/monitoring wells. The testbed was characterized geophysically and hydrologically, and three stimulations have been performed using the Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP) tool to measure strains, and a new strain quantifying tool (downhole robotic strain analysis tool -DORSA) was deployed in a monitoring hole during stimulation. Real-time data were broadcast during stimulations to allow real-time response to arising issues.

EGS Collab, Enhanced Geothermal Systems, EGS, fiel↗
DOE OSTI2022

Geological activity shapes the microbiome in deep-subsurface aquifers by advection

Subsurface environments host diverse microorganisms in fluid-filled fractures; however, little is known about how geological and hydrological processes shape the subterranean biosphere. Here, we sampled three flowing boreholes weekly for 10 mo in a 1478-m-deep fractured rock aquifer to study the role of fracture activity (defined as seismically or aseismically induced fracture aperture change) and advection on fluid-associated microbial community composition. We found that despite a largely stable deep-subsurface fluid microbiome, drastic community-level shifts occurred after events signifying physical changes in the permeable fracture network. The community-level shifts include the emergence of microbial families from undetected to over 50% relative abundance, as well as the replacement of the community in one borehole by the earlier community from a different borehole. Null-model analysis indicates that the observed spatial and temporal community turnover was primarily driven by stochastic processes (as opposed to deterministic processes). We, therefore, conclude that the observed community-level shifts resulted from the physical transport of distinct microbial communities from other fracture(s) that outpaced environmental selection. Given that geological activity is a major cause of fracture activity and that geological activity is ubiquitous across space and time on Earth, our findings suggest that advection induced by geological activity is a general mechanism shaping the microbial biogeography and diversity in deep-subsurface habitats across the globe.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Thermal and Mechanical Energy Performance Analysis of Closed-loop Systems in Hot-Dry-Rock and Hot-Wet-Rock Reservoirs

To understand the potential and limitations for recovering thermal and mechanical energy from closed-loop geothermal systems a collaborative study is underway that will investigate an array of system configurations, working fluids, geothermal reservoir characteristics, operational periods, and heat transfer enhancements. Closed-loop geothermal systems are distinguished from hydrothermal or enhanced geothermal systems (EGS) in that the working fluid only circulates through drilled boreholes. Principal objectives of this study are to determine upper limits for thermal and mechanical energy recovery and optimal operational and configuration parameters for each scenario. Teams of scientists and engineers are applying a suite of numerical simulation and analytical tools to model the heat recovery from closed-loop geothermal systems, and then optimizing operational and configuration parameters to maximize the thermal and mechanical energy recovery. Results from the suite of numerical simulators and analytical tools, such as outlet and inlet states and temperature profiles in the geothermal reservoir over time are intercompared to increase confidence in the analysis. This paper documents the study findings for closed-loop systems in hot-dry-rock and hot-wet-rock reservoirs, where water is the working fluid. The characteristics of the hot-dry-rock reservoir were based on the U.S. Department of Energy’s Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site, near Milford Utah. Two objective functions are defined to optimize the operational and configuration parameters of the system, one each for the recovery of mechanical and thermal energy over the period of operation. For both objective functions, a surface plant thermal to mechanical energy conversion factor and an energy drilling cost is required. In keeping with the study objectives the surface plant conversion factor is determined from a second-law of thermodynamics analysis of a generic binary plant, and drilling costs are based on those from the Utah FORGE site and current national electrical costs.

Closed-loop geothermal systems, hot-dry-rock, hot-↗
DOE OSTI2021

Systems, methods and tools for subterranean electrochemical characterization and enthalpy measurement in geothermal reservoirs

The present disclosure is directed to systems, methods and tools that measure ionic concentrations and downhole enthalpy of a flowing geothermal fluid in real-time at high-temperature and pressure. The systems, methods and tools include measuring the concentration of selected naturally occurring ions found in the liquid phase of the geothermal fluid throughout the wellbore using novel electrochemical sensor technologies. The change in liquid-phase ion concentration will be used to calculate the proportion of liquid to steam and allow for accurate enthalpy measurements. The techniques and technologies described here can be applied to any application of electrochemical sensing in extreme environments.

Cieslewski, Grzegorz↗
DOE OSTI2021

Geo Thermal Cloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The project is motivated by the challenges, risks, and costs associated with geothermal exploration and production. Many processes and parameters impacting geothermal conditions are poorly understood. Diverse datasets are available to help characterize subsurface geothermal conditions (public and proprietary; satellite, airborne surveys, vegetation/water sampling, geological, geophysical, etc.). Yet, it is not clear how to properly leverage these datasets for geothermal exploration due to an incomplete understanding of how physical processes impacting subsurface geothermal conditions are represented in these observations. Recent advancements in machine learning (ML) provide great promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. Our goals and work under Phases 1 and 2 (as proposed) of this project address all these needs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

The EGS Collab Project: Learnings from Experiment 1

The primary objective of the EGS Collab Project sponsored by DOE is to increase the understanding needed to efficiently implement enhanced geothermal systems (EGS). One goal of the EGS Collab project is to create a collaborative research environment in which to study stimulation of crystalline rock at the 10 meter scale. Key to this effort is the collection of high quality data to allow comparison to numerical coupled process models in an effort to build confidence in the codes and modeling techniques used. In response to this, the EGS Collab team has created an underground test bed at the Sanford Underground Research Facility (SURF) in Lead SD at a depth of approximately 1.5 km to examine hydraulic fracturing (Experiment 1). We are currently designing a second test bed aimed at investigating shear stimulation (Experiment 2). At the Experiment 1 location, we have characterized our host rock using laboratory testing and numerous field-based geophysical and geological techniques, and created a well-instrumented test bed to allow us to carefully monitor stimulation events and flow tests. In addition to the installed geophysical sensors, we have used tracer tests, differences in the ambient microbial communities at flow collection locations, and cold water injection to inform us about dynamic flow pathways. In Experiment 1, we have hydraulically stimulated the host rock in a number times at several locations in one well, creating new fractures that connect to existing fractures between the injection and production boreholes. We have performed long-term ambient and chilled water injection tests as an analog to EGS, and have monitored system changes resulting from these water injections through geophysical monitoring, flow and pressure measurements, tracer tests, and microbiology. Here, we summarize the tests performed, issues identified including poroelastic and thermoelastic effects, Joule-Thomson effects, restarting effects, indications of flow channeling, and the primary learnings from Experiment 1.

Enhanced Geothermal Systems, EGS Collab, stimulati↗
DOE OSTI2020

Microbial Community Composition in Deep‐Subsurface Reservoir Fluids Reveals Natural Interwell Connectivity

Abstract The identification of natural fractures and the wells they connect is crucial for the development of geological reservoirs because it may have an important impact on reservoir model construction and hydraulic fracture propagation. In this study we investigated the use of a novel data source, the microbial community composition in the reservoir formation fluids, for identification of interwell connectivity caused by natural fractures. We verified this concept at a newly developed mesoscale enhanced geothermal system (EGS) field testbed located 4,850 ft (1,478 m) beneath ground surface at the Sanford Underground Research Facility in Lead, SD. Fluids produced at or near the EGS testbed were sampled and subjected to high‐throughput 16S rRNA gene amplicon sequencing to analyze the microbial community profile therein. Despite the typically substantial heterogeneity across the community profiles of samples spatially distributed (10 m to 1.9 km apart) throughout the site, samples from two wells at the EGS testbed showed highly similar microbial community composition, suggesting the two wells intersected the same natural fracture. This evidence of natural connectivity between the two wells at the EGS testbed was later corroborated by core log analysis and sewer camera surveys into the boreholes. Besides the field case described in this study, microbial community composition as a reservoir diagnostic tool would be applicable in a much broader context such as unconventional hydrocarbon exploitation, groundwater reservoir characterization, and environmental remediation, adding valuable “hard” data capable of pinpointing the origins of fluids unambiguously.

Zhang, Yuran↗