Characteristics in hypocenters of microseismic events due to hydraulic fracturing and natural faults: a case study in the Horn River ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-09-12

AUTHORS

Jeong-Ung Woo, Juhwan Kim, Junkee Rhie, Tae-Seob Kang

ABSTRACT

For two to three decades, microseismic monitoring has been popular in the development of unconventional resources, because the fracture network generated by hydraulic fracturing mainly controls the productivity, and microseismic monitoring enables direct measurements for imaging the fracture network. Nevertheless, some refinements are required to make this method more practical. One challenge is to quantify the effects of pre-existing natural fractures for generating microseismic events. We determine the hypocenters of microseismic events occurring in a shale gas play in the Horn River Basin, Canada, and report several interesting spatial and temporal features of the hypocenter distributions. Automatic phase-picking is applied to waveform data recorded at 98 shallow buried three-component geophones, and phases thought to be from the same event are associated. The initial hypocenters of events are determined by iterative linear inversion algorithm then relocated using a double-difference algorithm, where relative travel time measurements are obtained with the waveform cross-correlation. We group events into many clusters based on fracking stages and their hypocenters, and then define the best-fitting plane of hypocenters for each cluster. Most strikes of the best-fitting planes are consistent with the direction of local horizontal stress maximum, indicating that hydraulic fracturing induces most microseismic events. However, the best-fitting planes of several clusters have strikes similar to those of pre-existing faults or fractures, indicating that pre-existing natural faults or fractures can affect the generation of microseismic events. In addition, some observations suggest that natural fractures can affect the temporal evolution of the spatial occurrence pattern of microseismic events. We observed specific migration patterns of microseismic events around known faults in the study area. Although further work is required for complete understanding of this phenomenon, our observations help elucidate the nature of microseismic generation. More... »

PAGES

683-694

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12303-017-0021-9

DOI

http://dx.doi.org/10.1007/s12303-017-0021-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091610037


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0404", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geophysics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "School of Earth and Environmental Sciences, Seoul National University, 08826, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "School of Earth and Environmental Sciences, Seoul National University, 08826, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Woo", 
        "givenName": "Jeong-Ung", 
        "id": "sg:person.011324661520.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011324661520.93"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Earth and Environmental Sciences, Seoul National University, 08826, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "School of Earth and Environmental Sciences, Seoul National University, 08826, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kim", 
        "givenName": "Juhwan", 
        "id": "sg:person.010416633005.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010416633005.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Earth and Environmental Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 08826, Seoul, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.31501.36", 
          "name": [
            "School of Earth and Environmental Sciences, Seoul National University, 08826, Seoul, Republic of Korea", 
            "School of Earth and Environmental Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 08826, Seoul, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rhie", 
        "givenName": "Junkee", 
        "id": "sg:person.011715664077.89", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011715664077.89"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Earth and Environmental Sciences, Pukyong National University, 48513, Busan, Republic of Korea", 
          "id": "http://www.grid.ac/institutes/grid.412576.3", 
          "name": [
            "Department of Earth and Environmental Sciences, Pukyong National University, 48513, Busan, Republic of Korea"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kang", 
        "givenName": "Tae-Seob", 
        "id": "sg:person.013154060005.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013154060005.81"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2017-09-12", 
    "datePublishedReg": "2017-09-12", 
    "description": "For two to three decades, microseismic monitoring has been popular in the development of unconventional resources, because the fracture network generated by hydraulic fracturing mainly controls the productivity, and microseismic monitoring enables direct measurements for imaging the fracture network. Nevertheless, some refinements are required to make this method more practical. One challenge is to quantify the effects of pre-existing natural fractures for generating microseismic events. We determine the hypocenters of microseismic events occurring in a shale gas play in the Horn River Basin, Canada, and report several interesting spatial and temporal features of the hypocenter distributions. Automatic phase-picking is applied to waveform data recorded at 98 shallow buried three-component geophones, and phases thought to be from the same event are associated. The initial hypocenters of events are determined by iterative linear inversion algorithm then relocated using a double-difference algorithm, where relative travel time measurements are obtained with the waveform cross-correlation. We group events into many clusters based on fracking stages and their hypocenters, and then define the best-fitting plane of hypocenters for each cluster. Most strikes of the best-fitting planes are consistent with the direction of local horizontal stress maximum, indicating that hydraulic fracturing induces most microseismic events. However, the best-fitting planes of several clusters have strikes similar to those of pre-existing faults or fractures, indicating that pre-existing natural faults or fractures can affect the generation of microseismic events. In addition, some observations suggest that natural fractures can affect the temporal evolution of the spatial occurrence pattern of microseismic events. We observed specific migration patterns of microseismic events around known faults in the study area. Although further work is required for complete understanding of this phenomenon, our observations help elucidate the nature of microseismic generation.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s12303-017-0021-9", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136627", 
        "issn": [
          "1226-4806", 
          "1598-7477"
        ], 
        "name": "Geosciences Journal", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "21"
      }
    ], 
    "keywords": [
      "fracture network", 
      "travel time measurements", 
      "linear inversion algorithm", 
      "microseismic events", 
      "inversion algorithm", 
      "Horn River Basin", 
      "waveform cross correlation", 
      "initial hypocenters", 
      "natural fractures", 
      "specific migration patterns", 
      "pre-existing natural fractures", 
      "cross correlation", 
      "microseismic monitoring", 
      "waveform data", 
      "algorithm", 
      "three-component geophones", 
      "spatial occurrence patterns", 
      "temporal evolution", 
      "plane", 
      "hypocenter distribution", 
      "time measurements", 
      "stress maximum", 
      "unconventional resources", 
      "network", 
      "double-difference algorithm", 
      "clusters", 
      "hydraulic fracturing", 
      "faults", 
      "direct measurement", 
      "measurements", 
      "distribution", 
      "hypocenters", 
      "observations", 
      "phenomenon", 
      "geophones", 
      "direction", 
      "case study", 
      "natural faults", 
      "maximum", 
      "evolution", 
      "shale gas plays", 
      "most strikes", 
      "generation", 
      "complete understanding", 
      "refinement", 
      "same event", 
      "work", 
      "fracturing", 
      "features", 
      "nature", 
      "phase", 
      "group events", 
      "gas plays", 
      "data", 
      "occurrence patterns", 
      "temporal features", 
      "further work", 
      "characteristics", 
      "strike", 
      "River Basin", 
      "patterns", 
      "effect", 
      "resources", 
      "events", 
      "addition", 
      "decades", 
      "pre-existing faults", 
      "shallow", 
      "basin", 
      "understanding", 
      "area", 
      "challenges", 
      "monitoring", 
      "study area", 
      "stage", 
      "development", 
      "study", 
      "fractures", 
      "migration patterns", 
      "play", 
      "productivity", 
      "Canada", 
      "method"
    ], 
    "name": "Characteristics in hypocenters of microseismic events due to hydraulic fracturing and natural faults: a case study in the Horn River Basin, Canada", 
    "pagination": "683-694", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1091610037"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12303-017-0021-9"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12303-017-0021-9", 
      "https://app.dimensions.ai/details/publication/pub.1091610037"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:34", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_720.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s12303-017-0021-9"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12303-017-0021-9'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12303-017-0021-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12303-017-0021-9'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12303-017-0021-9'


 

This table displays all metadata directly associated to this object as RDF triples.

166 TRIPLES      20 PREDICATES      107 URIs      99 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12303-017-0021-9 schema:about anzsrc-for:04
2 anzsrc-for:0404
3 schema:author N8809097f308343d7837e104d80e2dc18
4 schema:datePublished 2017-09-12
5 schema:datePublishedReg 2017-09-12
6 schema:description For two to three decades, microseismic monitoring has been popular in the development of unconventional resources, because the fracture network generated by hydraulic fracturing mainly controls the productivity, and microseismic monitoring enables direct measurements for imaging the fracture network. Nevertheless, some refinements are required to make this method more practical. One challenge is to quantify the effects of pre-existing natural fractures for generating microseismic events. We determine the hypocenters of microseismic events occurring in a shale gas play in the Horn River Basin, Canada, and report several interesting spatial and temporal features of the hypocenter distributions. Automatic phase-picking is applied to waveform data recorded at 98 shallow buried three-component geophones, and phases thought to be from the same event are associated. The initial hypocenters of events are determined by iterative linear inversion algorithm then relocated using a double-difference algorithm, where relative travel time measurements are obtained with the waveform cross-correlation. We group events into many clusters based on fracking stages and their hypocenters, and then define the best-fitting plane of hypocenters for each cluster. Most strikes of the best-fitting planes are consistent with the direction of local horizontal stress maximum, indicating that hydraulic fracturing induces most microseismic events. However, the best-fitting planes of several clusters have strikes similar to those of pre-existing faults or fractures, indicating that pre-existing natural faults or fractures can affect the generation of microseismic events. In addition, some observations suggest that natural fractures can affect the temporal evolution of the spatial occurrence pattern of microseismic events. We observed specific migration patterns of microseismic events around known faults in the study area. Although further work is required for complete understanding of this phenomenon, our observations help elucidate the nature of microseismic generation.
7 schema:genre article
8 schema:isAccessibleForFree false
9 schema:isPartOf N81c6119aa67e46d28338915ef1e2f0f8
10 N96211996efa343ad9ff5ebbe3bde16a6
11 sg:journal.1136627
12 schema:keywords Canada
13 Horn River Basin
14 River Basin
15 addition
16 algorithm
17 area
18 basin
19 case study
20 challenges
21 characteristics
22 clusters
23 complete understanding
24 cross correlation
25 data
26 decades
27 development
28 direct measurement
29 direction
30 distribution
31 double-difference algorithm
32 effect
33 events
34 evolution
35 faults
36 features
37 fracture network
38 fractures
39 fracturing
40 further work
41 gas plays
42 generation
43 geophones
44 group events
45 hydraulic fracturing
46 hypocenter distribution
47 hypocenters
48 initial hypocenters
49 inversion algorithm
50 linear inversion algorithm
51 maximum
52 measurements
53 method
54 microseismic events
55 microseismic monitoring
56 migration patterns
57 monitoring
58 most strikes
59 natural faults
60 natural fractures
61 nature
62 network
63 observations
64 occurrence patterns
65 patterns
66 phase
67 phenomenon
68 plane
69 play
70 pre-existing faults
71 pre-existing natural fractures
72 productivity
73 refinement
74 resources
75 same event
76 shale gas plays
77 shallow
78 spatial occurrence patterns
79 specific migration patterns
80 stage
81 stress maximum
82 strike
83 study
84 study area
85 temporal evolution
86 temporal features
87 three-component geophones
88 time measurements
89 travel time measurements
90 unconventional resources
91 understanding
92 waveform cross correlation
93 waveform data
94 work
95 schema:name Characteristics in hypocenters of microseismic events due to hydraulic fracturing and natural faults: a case study in the Horn River Basin, Canada
96 schema:pagination 683-694
97 schema:productId N588534c7ab4d4f3383cd0a07d9f2b304
98 N6517b6c3afe34ef7be2ac84b2bba8486
99 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091610037
100 https://doi.org/10.1007/s12303-017-0021-9
101 schema:sdDatePublished 2022-12-01T06:34
102 schema:sdLicense https://scigraph.springernature.com/explorer/license/
103 schema:sdPublisher Nda89179b475a4ad98154c657679d9fa0
104 schema:url https://doi.org/10.1007/s12303-017-0021-9
105 sgo:license sg:explorer/license/
106 sgo:sdDataset articles
107 rdf:type schema:ScholarlyArticle
108 N588534c7ab4d4f3383cd0a07d9f2b304 schema:name dimensions_id
109 schema:value pub.1091610037
110 rdf:type schema:PropertyValue
111 N6517b6c3afe34ef7be2ac84b2bba8486 schema:name doi
112 schema:value 10.1007/s12303-017-0021-9
113 rdf:type schema:PropertyValue
114 N715f60d121014a4682a30d1c7b9fc550 rdf:first sg:person.013154060005.81
115 rdf:rest rdf:nil
116 N72a9f5cc18024572b2d624b9895bd111 rdf:first sg:person.010416633005.29
117 rdf:rest Nd1d0ddc4b20c4416932248e75c7c3927
118 N81c6119aa67e46d28338915ef1e2f0f8 schema:volumeNumber 21
119 rdf:type schema:PublicationVolume
120 N8809097f308343d7837e104d80e2dc18 rdf:first sg:person.011324661520.93
121 rdf:rest N72a9f5cc18024572b2d624b9895bd111
122 N96211996efa343ad9ff5ebbe3bde16a6 schema:issueNumber 5
123 rdf:type schema:PublicationIssue
124 Nd1d0ddc4b20c4416932248e75c7c3927 rdf:first sg:person.011715664077.89
125 rdf:rest N715f60d121014a4682a30d1c7b9fc550
126 Nda89179b475a4ad98154c657679d9fa0 schema:name Springer Nature - SN SciGraph project
127 rdf:type schema:Organization
128 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
129 schema:name Earth Sciences
130 rdf:type schema:DefinedTerm
131 anzsrc-for:0404 schema:inDefinedTermSet anzsrc-for:
132 schema:name Geophysics
133 rdf:type schema:DefinedTerm
134 sg:journal.1136627 schema:issn 1226-4806
135 1598-7477
136 schema:name Geosciences Journal
137 schema:publisher Springer Nature
138 rdf:type schema:Periodical
139 sg:person.010416633005.29 schema:affiliation grid-institutes:grid.31501.36
140 schema:familyName Kim
141 schema:givenName Juhwan
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010416633005.29
143 rdf:type schema:Person
144 sg:person.011324661520.93 schema:affiliation grid-institutes:grid.31501.36
145 schema:familyName Woo
146 schema:givenName Jeong-Ung
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011324661520.93
148 rdf:type schema:Person
149 sg:person.011715664077.89 schema:affiliation grid-institutes:grid.31501.36
150 schema:familyName Rhie
151 schema:givenName Junkee
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011715664077.89
153 rdf:type schema:Person
154 sg:person.013154060005.81 schema:affiliation grid-institutes:grid.412576.3
155 schema:familyName Kang
156 schema:givenName Tae-Seob
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013154060005.81
158 rdf:type schema:Person
159 grid-institutes:grid.31501.36 schema:alternateName School of Earth and Environmental Sciences, Seoul National University, 08826, Seoul, Republic of Korea
160 School of Earth and Environmental Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 08826, Seoul, Republic of Korea
161 schema:name School of Earth and Environmental Sciences, Seoul National University, 08826, Seoul, Republic of Korea
162 School of Earth and Environmental Sciences, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 08826, Seoul, Republic of Korea
163 rdf:type schema:Organization
164 grid-institutes:grid.412576.3 schema:alternateName Department of Earth and Environmental Sciences, Pukyong National University, 48513, Busan, Republic of Korea
165 schema:name Department of Earth and Environmental Sciences, Pukyong National University, 48513, Busan, Republic of Korea
166 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...