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Title: Fracture Characterization in Enhanced Geothermal Systems by Wellbore and Reservoir Analysis

Abstract

This report highlights the work that was done to characterize fractured geothermal reservoirs using production data. That includes methods that were developed to infer characteristic functions from production data and models that were designed to optimize reinjection scheduling into geothermal reservoirs, based on these characteristic functions. The characterization method provides a robust way of interpreting tracer and flow rate data from fractured reservoirs. The flow-rate data are used to infer the interwell connectivity, which describes how injected fluids are divided between producers in the reservoir. The tracer data are used to find the tracer kernel for each injector-producer connection. The tracer kernel describes the volume and dispersive properties of the interwell flow path. A combination of parametric and nonparametric regression methods were developed to estimate the tracer kernels for situations where data is collected at variable flow-rate or variable injected concentration conditions. The characteristic functions can be used to calibrate thermal transport models, which can in turn be used to predict the productivity of geothermal systems. This predictive model can be used to optimize injection scheduling in a geothermal reservoir, as is illustrated in this report.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Stanford Univ., CA (United States). Dept. of Energy Resources Engineering
Sponsoring Org.:
USDOE
OSTI Identifier:
1050872
Report Number(s):
DOE-08GO18192
DOE Contract Number:  
FG36-08GO18192
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
15 GEOTHERMAL ENERGY; fractures, nanosensors, resistivity, enhanced geothermal systems

Citation Formats

Horne, Roland N., Li, Kewen, Alaskar, Mohammed, Ames, Morgan, Co, Carla, Juliusson, Egill, and Magnusdottir, Lilja. Fracture Characterization in Enhanced Geothermal Systems by Wellbore and Reservoir Analysis. United States: N. p., 2012. Web. doi:10.2172/1050872.
Horne, Roland N., Li, Kewen, Alaskar, Mohammed, Ames, Morgan, Co, Carla, Juliusson, Egill, & Magnusdottir, Lilja. Fracture Characterization in Enhanced Geothermal Systems by Wellbore and Reservoir Analysis. United States. doi:10.2172/1050872.
Horne, Roland N., Li, Kewen, Alaskar, Mohammed, Ames, Morgan, Co, Carla, Juliusson, Egill, and Magnusdottir, Lilja. Sat . "Fracture Characterization in Enhanced Geothermal Systems by Wellbore and Reservoir Analysis". United States. doi:10.2172/1050872. https://www.osti.gov/servlets/purl/1050872.
@article{osti_1050872,
title = {Fracture Characterization in Enhanced Geothermal Systems by Wellbore and Reservoir Analysis},
author = {Horne, Roland N. and Li, Kewen and Alaskar, Mohammed and Ames, Morgan and Co, Carla and Juliusson, Egill and Magnusdottir, Lilja},
abstractNote = {This report highlights the work that was done to characterize fractured geothermal reservoirs using production data. That includes methods that were developed to infer characteristic functions from production data and models that were designed to optimize reinjection scheduling into geothermal reservoirs, based on these characteristic functions. The characterization method provides a robust way of interpreting tracer and flow rate data from fractured reservoirs. The flow-rate data are used to infer the interwell connectivity, which describes how injected fluids are divided between producers in the reservoir. The tracer data are used to find the tracer kernel for each injector-producer connection. The tracer kernel describes the volume and dispersive properties of the interwell flow path. A combination of parametric and nonparametric regression methods were developed to estimate the tracer kernels for situations where data is collected at variable flow-rate or variable injected concentration conditions. The characteristic functions can be used to calibrate thermal transport models, which can in turn be used to predict the productivity of geothermal systems. This predictive model can be used to optimize injection scheduling in a geothermal reservoir, as is illustrated in this report.},
doi = {10.2172/1050872},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {6}
}