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Author: Egill Júlíusson
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Languages : en
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Reservoir characterization is one of the most important and challenging parts of running a successful geothermal operation. Characterization requires thorough understanding of the physics that govern the flow of mass and energy through the reservoir. As for most subsurface modeling endeavors, the inability to measure the actual value of properties in the geothermal system make it necessary to strike a balance between what is included in the reservoir model and what is known about the physical processes that might take place in the subsurface. This balance should reflect the decisions that need to be made based on the model, and the data available for model calibration. In this work, a number of methods were developed for characterizing well-to-well connections in fractured geothermal reservoirs. These methods were based on production data that are commonly recorded in geothermal fields, i.e. pressure, flow rate, tracer and temperature. A key aspect in the developing this work, for multiwell applications, was to find the link between the various types of models, and understand how they could be combined to estimate well-to-well properties. The estimation of these properties relied on regression analysis, where an effort was made to balance the complexity of the regression model with the information required from the given data source. The combined characterization defined a work flow that would be well-suited to characterize fractured geothermal systems, with low compressibility characteristics. An effort was made to illustrate the usefulness of the characterization method to tackle important reservoir engineering problems. This was done by formulating and solving a flow rate scheduling problem for a geothermal field. The results showed that considerable gains in efficiency could be made, given a set of well-calibrated interwell relationships.
Author: Egill Júlíusson
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Reservoir characterization is one of the most important and challenging parts of running a successful geothermal operation. Characterization requires thorough understanding of the physics that govern the flow of mass and energy through the reservoir. As for most subsurface modeling endeavors, the inability to measure the actual value of properties in the geothermal system make it necessary to strike a balance between what is included in the reservoir model and what is known about the physical processes that might take place in the subsurface. This balance should reflect the decisions that need to be made based on the model, and the data available for model calibration. In this work, a number of methods were developed for characterizing well-to-well connections in fractured geothermal reservoirs. These methods were based on production data that are commonly recorded in geothermal fields, i.e. pressure, flow rate, tracer and temperature. A key aspect in the developing this work, for multiwell applications, was to find the link between the various types of models, and understand how they could be combined to estimate well-to-well properties. The estimation of these properties relied on regression analysis, where an effort was made to balance the complexity of the regression model with the information required from the given data source. The combined characterization defined a work flow that would be well-suited to characterize fractured geothermal systems, with low compressibility characteristics. An effort was made to illustrate the usefulness of the characterization method to tackle important reservoir engineering problems. This was done by formulating and solving a flow rate scheduling problem for a geothermal field. The results showed that considerable gains in efficiency could be made, given a set of well-calibrated interwell relationships.
Author: Lilja Magnúsdóttir
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Languages : en
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The configuration of fractures in a geothermal reservoir is central to the performance of the system. The interconnected fractures control the heat and mass transport in the reservoir and if the fluid reaches production wells before it is fully heated, unfavorable effects on energy production may result due to decreasing fluid enthalpies. Consequently, inappropriate placing of injection or production wells can lead to premature thermal breakthrough. Thus, fracture characterization in geothermal reservoirs is an important task in order to design the recovery strategy appropriately and increase the overall efficiency of the power production. This is true both in naturally fractured geothermal systems as well as in Enhanced Geothermal Systems (EGS) with man-made fractures produced by hydraulic stimulation. In this study, the aim was to estimate fracture connectivity in geothermal reservoirs using a conductive fluid injection and an inversion of time-lapse electric potential data. Discrete fracture networks were modeled and a flow simulator was used first to simulate the flow of a conductive tracer through the reservoirs. Then, the simulator was applied to solve the electric fields at each time step by utilizing the analogy between Ohm's law and Darcy's law. The electric potential difference between well-pairs drops as a conductive fluid fills fracture paths from the injector towards the producer. Therefore, the time-lapse electric potential data can be representative of the connectivity of the fracture network. Flow and electric simulations were performed on models of various fracture networks and inverse modeling was used to match reservoir models to other fracture networks in a library of networks by comparing the time-histories of the electric potential. Two fracture characterization indices were investigated for describing the character of the fractured reservoirs; the fractional connected area and the spatial fractal dimension. In most cases, the electrical potential approach was used successfully to estimate both the fractional connected area of the reservoirs and the spatial fractal dimension. The locations of the linked fracture sets were also predicted correctly. Next, the electric method was compared to using only the simple tracer return curves at the producers in the inverse analysis. The study showed that the fracture characterization indices were estimated somewhat better using the electric approach. The locations of connected areas in the predicted network were also in many cases incorrect when only the tracer return curves were used. The use of the electric approach to predict thermal return was investigated and compared to using just the simple tracer return curves. The electric approach predicted the thermal return curves relatively accurately. However, in some cases the tracer return gave a better estimation of the thermal behavior. The electric measurements are affected by both the time it takes for the conductive tracer to reach the production well, as well as the overall location of the connected areas. When only the tracer return curves are used in the inverse analysis, only the concentration of tracer at the producer is measured but there is a good correlation between the tracer breakthrough time and the thermal breakthrough times. Thus, the tracer return curves can predict the thermal return accurately but the overall location of fractures might not be predicted correctly. The electric data and the tracer return data were also used together in an inverse analysis to predict the thermal returns. The results were in some cases somewhat better than using only the tracer return curves or only the electric data. A different injection scheme was also tested for both approaches. The electric data characterized the overall fracture network better than the tracer return curves so when the well pattern was changed from what was used during the tracer and electric measurements, the electric approach predicted the new thermal return better. In addition, the thermal return was predicted considerably better using the electric approach when measurements over a shorter period of time were used in the inverse analysis. In addition to characterizing the fracture distribution better, the electric approach can give information about the conductive fluid flowing through the fracture network even before it has reached the production wells.
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Languages : en
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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.
Author: Ronald Nelson
Publisher: Elsevier
ISBN: 0080507298
Category : Technology & Engineering
Languages : en
Pages : 353
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Book Description
Geologists, engineers, and petrophysicists concerned with hydrocarbon production from naturally fractured reservoirs will find this book a valuable tool for obtaining pertinent rock data to evaluate reserves and optimize well location and performance. Nelson emphasizes geological, petrophysical, and rock mechanics to complement other studies of the subject that use well logging and classical engineering approaches. This well organized, updated edition contains a wealth of field and laboratory data, case histories, and practical advice. - A great how-to-guide for anyone working with fractured or highly anisotropic reservoirs - Provides real-life illustrations through case histories and field and laboratory data
Author: H. M. Haitjema
Publisher: Elsevier
ISBN: 0080499104
Category : Mathematics
Languages : en
Pages : 407
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Book Description
Modeling has become an essential tool for the groundwater hydrologist. Where field data is limited, the analytic element method (AEM) is rapidly becoming the modeling method of choice, especially given the availability of affordable modeling software. Analytic Element Modeling of Groundwater Flow provides all the basics necessary to approach AEM successfully, including a presentation of fundamental concepts and a thorough introduction to Dupuit-Forchheimerflow. This book is unique in its emphasis on the actual use of analytic element models. Real-world examples complement material presented in the text. An educational version of the analytic element program GFLOW is included to allow the reader to reproduce the various solutions to groundwater flow problems discussed in the text. Researchers and graduate students in groundwater hydrology, geology, andengineering will find this book an indispensable resource. * * Provides a fundamental introduction to the use of the analytic element method. * Offers a step-by-step approach to groundwater flow modeling. * Includes an educational version of the GFLOW modeling software.
Author: Lawrence Berkeley Laboratory
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Author: Kan Wu
Publisher: Elsevier
ISBN: 0323953611
Category : Technology & Engineering
Languages : en
Pages : 296
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Book Description
Fiber optic-based measurements are innovative tools for the oil and gas industry to utilize in monitoring wells in a variety of applications including geothermal activity. Monitoring unconventional reservoirs is still challenging due to complex subsurface conditions and current research focuses on qualitative interpretation of field data. Hydraulic Fracture Geometry Characterization from Fiber Optic-Based Strain Measurements delivers a critical reference for reservoir and completion engineers to better quantify the propagation process and evolution of fracture geometry with a forward model and novel inversion model. The reference reviews different fiber optic-based temperature, acoustic, and strain measurements for monitoring fracture behaviors and includes advantages and limitations of each measurement, giving engineers a better understanding of measurements applied in all types of subsurface formations. Stress/strain rate responses on rock deformation are given a holistic approach, including guidelines and an automatic algorithm for identification of fracture hits. Last, a novel inversion model is introduced to show how fracture geometry can be used for optimization on well placement decisions. Supported by case studies, Hydraulic Fracture Geometry Characterization from Fiber Optic-Based Strain Measurements gives today's engineers better understanding of all complex subsurface measurements through fiber optic technology. - Examine the basics of distributed fiber optic strain measurements - Conduct a detailed analysis of strain responses observed in both horizontal and vertical monitoring wells - Present a systematic approach for interpreting strain data measured in the field - Highlight the significant insights and values that can be derived from the field measured strain dataset - Support monitoring and modeling for subsurface energy extraction and safe storage
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Languages : en
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On the basis of shear wave splitting data recorded and processed from the Geysers and Coso geothermal fields, this project aims at developing a computer-based methodology to produce 3D maps of crack distribution, crack geometry, as well as crack density in fractured reservoirs. Such maps are crucial in identifying zones of high permeability in reservoirs as well as in determining fluid flow directions. The raw data for the project consists of seismographic recordings of microearthquakes (MEQ) detected over many years by arrays of sensors at both The Geysers and Coso reservoirs. With the experience acquired in the processing and interpretation of these data, we are developing a novel computer-based technology for the exploration of fractured reservoirs, which consists of the following software packages (three modules written in Matlab-compatible language): (1) Module one: Data processing package. (2) Module two: Forward modeling package. (3) Module three: Inverse modeling package.
Author: Ayşe Dönmez Zeybek
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Languages : en
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Reservoir description is an important step to make reliable reservoir performance predctons. In general, reservoir description can be defined as the process of describing various reservoir characteristics (e.g., porosity, permeabiIity, thickness,etc.) that honor all the available data (e.g., geological, geophysical, petrophysical,production data). The appropriate way to reach this goal is to integrate all available data from different sources; geologic knowledge, seismic data, well test pressure data, production history, etc. However, how to efficiently integrate all these data coming from different sources is a challenge to the person or team working inreservoir description area.Although, geostatistical methods (Kriging, cokriging, etc. ) are well-suited tointegrate static (1inear or hard) data coming from geology, logs, seismic and core analysis, they are limited when applied to dynamic data such as well test pressure,tracer test and production data. Because dynamic data are non-linear with respect tomodel parameters, conditioning to dynamic data possesses some difficulties. inrecent years, it has been shown that the inverse problem theory based on Bayesian estimation provides a powerful methodology not only to generate rock property fields conditioned to both static and dynamic data, but also to assess the uncertaintyin performance predictions. To date, standard applications of inverse problem theory given in the literature assume that rock property tields obey multinormal(Gaussian) distribution and are second order stationary. in this work, the main objective is to extend the inverse problem theory to cases where rock property tields( only porosity and permeabiIity tields are considered) come from fractal distributions so that one can be able to generate fractal rock property tields conditioned to both static and well test pressure data.Recent studies have shown that ftactals like fractional Gaussian noise (fGn) and fractional Brownian motion ( fBm) are promising approaches to characterize porosity and/or permeabiIity , in general the hydraulic property variations in the subsurface.in the literature, there exist stochastic interpolation methods that can be used to generate conditional fractal simulations honoring variograms and hard data(measurements of porosity and permeabiIity at wells). However, there exists nostudy in the literature that considers generating fractal flelds conditioned to dynamicdata, in particular to well-test pressure data. Thus, the objective of this work is togenerate fractal (fGn and fBm) porosity and permeability tields conditioned tovariograms, hard data and well-test pressure data by using the inverse problemtheory .The thesis is organized as follows. In Chapter 2, we begin by presenting the theoryof fractal and fractal distributions in detail. Because, in this work, we define rockproperty fields, specifically permeability and porosity , using the fractaldistributions, we note various statistical properties of fGn and ffim distributions.We also provide the methods that can be used to generate unconditional realizationsof isotropic or anisotropic ffim/fGn random functions. In Chapter 3, we review theinverse problem theory based on Bayesian estimation, because in this study, we usethis theory to generate rock property fields (porosity and permeability) conditioned to both well-test pressure, static ( core and well log) and geostatistical data. Becauseour objective in this study is to extend the inverse problem methodology to generateporosity and permeability fields that show fractal (fGn and ffim) behavior, we show how one can extend the commonly used inverse problem methodology based onstationary Gaussian fields to fractal fields. In Chapter 4, we present one, two, andthree dimensional applications of inverse problem theory based on Bayesian estimation (explained in Chapter 3) to generate fGn/fBm porosity and 1n-permeability fields conditioned to well-test pressure, static ( core and well log) and geostatistical data for single phase problems. In Chapter 5, we study transport over fracture network that can be considered as a fractal object. We present fractal pressure transient behavior and analysis of a single or multiple vertical well systems (with wellbore storage and skin effects) producing in fractured reservoirs without matrix participation. Nonlinear regression analysis for pressure data from such systems is presented and discussed for analysis purposes. Also, analysis of aninterference pressure data from Kizildere geothermal field using fractal model is presented.
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Languages : en
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As a new modeling procedure of geothermal energy extraction systems, the authors present two dimensional and three dimensional modeling techniques of subsurface fracture network, based on fractal geometry. Fluid flow in fractured rock occurs primarily through a connected network of discrete fractures. The fracture network approach, therefore, seeks to model fluid flow and heat transfer through such rocks directly. Recent geophysical investigations have revealed that subsurface fracture networks can be described by "fractal geometry". In this paper, a modeling procedure of subsurface fracture network is proposed based on fractal geometry. Models of fracture networks are generated by distributing fractures randomly, following the fractal relation between fracture length r and the number of fractures N expressed with fractal dimension D as N =C·r-D, where C is a constant to signify the fracture density of the rock mass. This procedure makes it possible to characterize geothermal reservoirs by the parameters measured from field data, such as core sampling. In this characterization, the fractal dimension D and the fracture density parameter C of a geothermal reservoir are used as parameters to model the subsurface fracture network. Using this model, the transmissivities between boreholes are also obtained as a function of the fracture density parameter C, and a parameter study of system performances, such as heat extraction, is performed. The results show the dependence of thermal recovery of geothermal reservoir on fracture density parameter C.
