Analysis of surface and seismic sources in dense array data with match field processing and Markov chain Monte Carlo sampling
Not provided.
Geochemistry & Geophysics↗
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Discovering new events beyond the catalogue—application of empirical matched field processing to Salton Sea geothermal field seismicity
Using empirical matched field processing (MFP), we compare 4 yr of continuous seismic data to a set of 195 master templates from within an active geothermal field and identify over 140 per cent more events than were identified using traditional detection and location techniques alone. In managed underground reservoirs, a substantial fraction of seismic events can be excluded from the official catalogue due to an inability to clearly identify seismic-phase onsets. Empirical MFP can improve the effectiveness of current seismic detection and location methodologies by using conventionally located events with higher signal-to-noise ratios as master events to define wavefield templates that could then be used to map normally discarded indistinct seismicity. Since MFP does not require picking, it can be carried out automatically and rapidly once suitable templates are defined. In this application, we extend MFP by constructing local-distance empirical master templates using Southern California Earthquake Data Center archived waveform data of events originating within the Salton Sea Geothermal Field. We compare the empirical templates to continuous seismic data collected between 1 January 2008 and 31 December 2011. The empirical MFP method successfully identifies 6249 additional events, while the original catalogue reported 4352 events. The majority of these new events are lower-magnitude events with magnitudes between M0.2–M0.8. Here, the increased spatial-temporal resolution of the microseismicity map within the geothermal field illustrates how empirical MFP, when combined with conventional methods, can significantly improve seismic network detection capabilities, which can aid in long-term sustainability and monitoring of managed underground reservoirs.
15 GEOTHERMAL ENERGY↗
Mapping Diffuse Seismicity Using Empirical Matched Field Processing Techniques
The objective of this project is to detect and locate more microearthquakes using the empirical matched field processing (MFP) method than can be detected using only conventional earthquake detection techniques. We propose that empirical MFP can complement existing catalogs and techniques. We test our method on continuous seismic data collected at the Salton Sea Geothermal Field during November 2009 and January 2010. In the Southern California Earthquake Data Center (SCEDC) earthquake catalog, 619 events were identified in our study area during this time frame and our MFP technique identified 1094 events. Therefore, we believe that the empirical MFP method combined with conventional methods significantly improves the network detection ability in an efficient matter.
15 GEOTHERMAL ENERGY↗
Mapping Diffuse Seismicity for Geothermal Reservoir Management with Matched Field Processing: Year 2 Report
Detecting and locating seismic events in managed underground geothermal reservoirs can become increasingly difficult during certain reservoir management operations. For example, during fluid injection procedures, large numbers of microearthquakes frequently occur during a relatively short amount of time. On seismic records, these events can overlap or have poor signal-to-noise ratios (SNR). This can obscure the onset of smaller signals and complicate the picking of seismic phases needed for earthquake location algorithms. To accurately illuminate the active areas of the underground reservoir through time however, a complete earthquake catalog down to very small magnitudes is necessary. To aid in the seismic characterization of reservoir fracture networks, we propose to complement traditional earthquake detection and location techniques with the empirical matched field processing (MFP) method. The principle of the empirical MFP method is to calculate propagation from source to receivers for pre-existing events, also called master templates. Then MFP matches the spatial structure of incoming seismicity observed by a network of sensors to master templates keyed to potential event locations. We demonstrate that empirical MFP can complement existing catalogs and techniques by using archived seismic information to increase earthquake catalog completeness. We identify and construct representative master templates using the Southern California Earthquake Data Center (SCEDC) earthquake catalog and archived waveform data of events originating within the Salton Sea Geothermal Field (SSGF). We identify 231 master events that have good quality seismic records at four or more seismic stations. We apply the empirical MFP method to continuous seismic data collected at the SSGF between November 2009 and December 2010. The MFP method successfully identified 5357 local events, while the original catalog only reported 1536 events. This increase in the number of events allows us to better determine the active fractures of the geothermal reservoir. We model the Coulomb stress change due to events between M2 – M3.5 that occurred during the January 2010 seismic swarm. Our results show a good correlation between regions of increased Coulomb stress change due to double-couple shear events on faults and the locations of subsequent microseismicity. In addition, we investigate the lower event detection magnitude threshold of our surface network using the MFP method. It clearly shows the event detection threshold is extended to include many smaller earthquakes with magnitudes down to M0.0. Therefore, we believe that the empirical MFP method, when combined with conventional methods, significantly improves network detection capabilities.
15 GEOTHERMAL ENERGY↗
Computational Complexity of Subspace Detectors and Matched Field Processing
Subspace detectors implement a correlation type calculation on a continuous (network or array) data stream [Harris, 2006]. The difference between subspace detectors and correlators is that the former projects the data in a sliding observation window onto a basis of template waveforms that may have a dimension (d) greater than one, and the latter projects the data onto a single waveform template. A standard correlation detector can be considered to be a degenerate (d=1) form of a subspace detector. Figure 1 below shows a block diagram for the standard formulation of a subspace detector. The detector consists of multiple multichannel correlators operating on a continuous data stream. The correlation operations are performed with FFTs in an overlap-add approach that allows the stream to be processed in uniform, consecutive, contiguous blocks. Figure 1 is slightly misleading for a calculation of computational complexity, as it is possible, when treating all channels with the same weighting (as shown in the figure), to perform the indicated summations in the multichannel correlators before the inverse FFTs and to get by with a single inverse FFT and overlap add calculation per multichannel correlator. In what follows, we make this simplification.
58 GEOSCIENCES↗
Discovering New Events Beyond the Catalog - Application of Matched Field Processing to Salton Sea Geothermal Field Seismicity
Abstract not provided
15 GEOTHERMAL ENERGY↗
Discovering New Events beyond the Catalog - Application of Matched Field Processing to Salton Sea Geothermal Field Seismicity
Abstract not provided
58 GEOSCIENCES↗
Characterization with dense array data of seismic sources in the shallow part of the San Jacinto fault zone
SUMMARY We analyse dominant sources identified in a catalogue of more than 156 000 localizations performed using a 26-d data set recorded by a dense array set on the San Jacinto fault near Anza, in California. Events were localized using an array processing technique called Match Field Processing. As for all array processing techniques, the quality of the event position decrease when the events are outside of the array. We thus separate localizations in and outside the array using simple geometrical conditions. We compare the time distribution of the localization to additional data such as meteorological data, day of human activity as well as existing catalogues to determine the nature of the dominant events located using our method. We find that most of the events located outside of the array could be attributed to a surface structure excited by wind. On the other hand, part of the localizations under the array occur during regional earthquakes and could correspond to diffraction on the fault's heterogeneities. The rest of the localizations inside the array could be generated by the fault itself.
Geochemistry & Geophysics↗
Induced seismicity during the 2012 Newberry EGS stimulation: Assessment of two advanced earthquake detection techniques at an EGS site
To improve the microearthquake detection capability during the 2012 Newberry enhanced geothermal system (EGS) demonstration, we applied two advanced earthquake detection algorithms to the continuous seismic data. These techniques have the power to detect microearthquakes within the seismic noise that would otherwise be missed using traditional techniques. We compare the efficacy of a matched field processing algorithm with a multi-channel waveform correlation algorithm. Both methods identified between 60%–80% more events, with each slightly outperforming the other in different seismic noise contexts. Incorporating either template-matching technique into a seismic processing routine could therefore effectively increase the sensitivity of the seismic network without changes to the existing hardware. Additionally, lowering the magnitude of completeness would significantly add to the number of events that could subsequently inform future decision making at the reservoir level.
15 GEOTHERMAL ENERGY↗
Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines
We report aftershock sequences following very large earthquakes present enormous challenges to near-real-time generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase-association algorithms and a significant deterioration in the quality of underlying, fully automatic event bulletins. Current processing pipelines were designed a generation ago, and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric data streams that are then scanned by a phase-association algorithm to form event hypotheses. We consider the scenario in which a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located, using a separate specially targeted semiautomatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid-search algorithm that may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase-association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove about half of the original detections that could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event hypotheses. Lastly, further reductions in the number of detections in the parametric data streams are likely, using correlation and subspace detectors and/or empirical matched field processing.
58 GEOSCIENCES↗
Microearthquake Studies at the Salton Sea Geothermal Field
The objective of this project is to detect and locate microearthquakes to aid in the characterization of reservoir fracture networks. Accurate identification and mapping of the large numbers of microearthquakes induced in EGS is one technique that provides diagnostic information when determining the location, orientation and length of underground crack systems for use in reservoir development and management applications. Conventional earthquake location techniques often are employed to locate microearthquakes. However, these techniques require labor-intensive picking of individual seismic phase onsets across a network of sensors. For this project we adapt the Matched Field Processing (MFP) technique to the elastic propagation problem in geothermal reservoirs to identify more and smaller events than traditional methods alone.
15 GEOTHERMAL ENERGY↗
Newberry Seismic Deployment Fieldwork Report
This report summarizes the seismic deployment of Lawrence Livermore National Laboratory (LLNL) Geotech GS-13 short-period seismometers at the Newberry Enhanced Geothermal System (EGS) Demonstration site located in Central Oregon. This Department of Energy (DOE) demonstration project is managed by AltaRock Energy Inc. AltaRock Energy had previously deployed Geospace GS-11D geophones at the Newberry EGS Demonstration site, however the quality of the seismic data was somewhat low. The purpose of the LLNL deployment was to install more sensitive sensors which would record higher quality seismic data for use in future seismic studies, such as ambient noise correlation, matched field processing earthquake detection studies, and general EGS microearthquake studies. For the LLNL deployment, seven three-component seismic stations were installed around the proposed AltaRock Energy stimulation well. The LLNL seismic sensors were connected to AltaRock Energy Gueralp CMG-DM24 digitizers, which are powered by AltaRock Energy solar panels and batteries. The deployment took four days in two phases. In phase I, the sites were identified, a cavity approximately 3 feet deep was dug and a flat concrete pad oriented to true North was made for each site. In phase II, we installed three single component GS-13 seismometers at each site, quality controlled the data to ensure that each station was recording data properly, and filled in each cavity with native soil.
15 GEOTHERMAL ENERGY↗
Mapping Glacier Basal Sliding Applying Machine Learning
During the RESOLVE project (“High-resolution imaging in subsurface geophysics: development of a multi-instrument platform for interdisciplinary research”), continuous surface displacement and seismic array observations were obtained on Glacier d’Argentière in the French Alps for 35 days in May 2018. The data set is used to perform a detailed study of targeted processes within the highly dynamic cryospheric environment. In particular, the physical processes controlling glacial basal motion are poorly understood and remain challenging to observe directly. Especially in the Alpine region for temperate based glaciers where the ice rapidly responds to changing climatic conditions and thus, processes are strongly intermittent in time and heterogeneous in space. Spatially dense seismic and Global Positioning System (GPS) measurements are analyzed applying machine learning to gain insight into the processes controlling glacial motions of Glacier d’Argentière. Using multiple bandpass-filtered copies of the continuous seismic waveforms, we compute energy-based features, develop a matched field beamforming catalog and include meteorological observations. Features describing the data are analyzed with a gradient boosting decision tree model to directly estimate the GPS displacements from the seismic noise. We posit that features of the seismic noise provide direct access to the dominant parameters that drive displacement on the highly variable and unsteady surface of the glacier. The machine learning model infers daily fluctuations and longer term trends. The results show on-ice displacement rates are strongly modulated by activity at the base of the glacier. The techniques presented provide a new approach to study glacial basal sliding and discover its full complexity.
58 GEOSCIENCES↗
Holographic matched filter for full-field in-line signal processing of optical-fiber sensor outputs
A holographic matched filter is used to measure the changes in the output of a dual-mode fiber undergoing axial strain. The hologram is formed by interfering collimated light from a single-mode reference fiber and an unstrained dual-mode sensor fiber on a small piece of holographic material. When the hologram is illuminated by the strained sensor fiber, the cross-correlation field comparing the dual-mode output and its previous state, as recorded in the hologram, is collected, focused into a pick-up fiber, and sent to a photodiode. This arrangement allows for a compact, in-line method for full-field processing of the strain-induced changes in phase and amplitude in the sensor fiber. Other uses of such fiber-based correlators are suggested.
Indebetouw, Guy↗
Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches
Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Surrogate models for development of unconventional shale reservoirs by an integrated numerical approach of hydraulic fracturing, flow and geomechanics, and machine learning
We develop well-completion surrogate models by taking an integrated workflow of hydraulic fracturing, flow, geomechanics, and machine learning simulation. There are three steps in the proposed workflow. First, history-matching processes are conducted with the field data including pumping and production data for characterization. Second, full-physics simulation is performed with various parameters of the field development (e.g., cluster spacing, clusters per stage, pumping rates and times, amount of proppant, and well spacing) to generate multiple simulation results by changing the parameters of the completion design with well-known hydraulic fracturing, reservoir, geomechanics simulators to calculate fracture geometry, reservoir depressurization, induced stress changes. The workflow is demonstrated over a field in the Southern Midland Basin. Here, we take two completion scenarios: a single well case followed by a multi-well case. Finally, a Long Short-Term Memory (LSTM) machine learning algorithm is employed to create surrogate models that can replicate the full-physics simulation results. Furthermore, results show that the trained models applied in the single well and multi-well cases for a particular geological system can provide good accuracy close to those provided by full-physics simulations. Specifically, the site-specific surrogate models can predict fracture parameters (length, height, and surface area) and cumulative production accurately with computational efficiency, suggesting our proposed workflow can be used as a pragmatic tool for expediting the well completion optimization process.
Geomechanics↗
Contribution to methods for calculating the flow about thin lifting wings at transonic speeds: Analytic expressions for the far field
The problem of determining the small-disturbance flow about two-dimensional airfoils at transonic speeds has been successfully treated by the process of matching a numerical solution of the near field to analytic expressions for the far field. The three-dimensional problem, it would appear, can be treated in a similar way with the aid of algorithms adapted to high-speed and high-capacity computers. The far-field potential for both lifting and nonlifting three-dimensional wings at transonic speeds is developed herein for a subsonic free stream. This potential could be used for a three-dimensional-wing computation similar to the computation made for the two-dimensional wing.
Klunker, E. B.↗
Efficient Aho-Corasick String Matching on Emerging Multicore Architectures
String matching algorithms are critical to several scientific fields. Beside text processing and databases, emerging applications such as DNA protein sequence analysis, data mining, information security software, antivirus, ma- chine learning, all exploit string matching algorithms [3]. All these applica- tions usually process large quantity of textual data, require high performance and/or predictable execution times. Among all the string matching algorithms, one of the most studied, especially for text processing and security applica- tions, is the Aho-Corasick algorithm. 1 2 Book title goes here Aho-Corasick is an exact, multi-pattern string matching algorithm which performs the search in a time linearly proportional to the length of the input text independently from pattern set size. However, depending on the imple- mentation, when the number of patterns increase, the memory occupation may raise drastically. In turn, this can lead to significant variability in the performance, due to the memory access times and the caching effects. This is a significant concern for many mission critical applications and modern high performance architectures. For example, security applications such as Network Intrusion Detection Systems (NIDS), must be able to scan network traffic against very large dictionaries in real time. Modern Ethernet links reach up to 10 Gbps, and malicious threats are already well over 1 million, and expo- nentially growing [28]. When performing the search, a NIDS should not slow down the network, or let network packets pass unchecked. Nevertheless, on the current state-of-the-art cache based processors, there may be a large per- formance variability when dealing with big dictionaries and inputs that have different frequencies of matching patterns. In particular, when few patterns are matched and they are all in the cache, the procedure is fast. Instead, when they are not in the cache, often because many patterns are matched and the caches are continuously thrashed, they should be retrieved from the system memory and the procedure is slowed down by the increased latency. Efficient implementations of string matching algorithms have been the fo- cus of several works, targeting Field Programmable Gate Arrays [4, 25, 15, 5], highly multi-threaded solutions like the Cray XMT [34], multicore proces- sors [19] or heterogeneous processors like the Cell Broadband Engine [35, 22]. Recently, several researchers have also started to investigate the use Graphic Processing Units (GPUs) for string matching algorithms in security applica- tions [20, 10, 32, 33]. Most of these approaches mainly focus on reaching high peak performance, or try to optimize the memory occupation, rather than looking at performance stability. However, hardware solutions supports only small dictionary sizes due to lack of memory and are difficult to customize, while platforms such as the Cell/B.E. are very complex to program.
Tumeo, Antonino↗