Full field reservoir modeling of shale assets using advanced data-driven analytics
Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems - induced or natural) lea...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2016-01-01
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| Series: | Geoscience Frontiers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987114001649 |
| _version_ | 1797763969224540160 |
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| author | Soodabeh Esmaili Shahab D. Mohaghegh |
| author_facet | Soodabeh Esmaili Shahab D. Mohaghegh |
| author_sort | Soodabeh Esmaili |
| collection | DOAJ |
| description | Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems - induced or natural) leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called “hard data” directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The “hard data” refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of “soft data” (non-measured, interpretive data such as frac length, width, height and conductivity) in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset. |
| first_indexed | 2024-03-12T19:49:00Z |
| format | Article |
| id | doaj.art-4b326ef6c78c4dd5bdab27bd450818e9 |
| institution | Directory Open Access Journal |
| issn | 1674-9871 |
| language | English |
| last_indexed | 2024-03-12T19:49:00Z |
| publishDate | 2016-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoscience Frontiers |
| spelling | doaj.art-4b326ef6c78c4dd5bdab27bd450818e92023-08-02T03:19:14ZengElsevierGeoscience Frontiers1674-98712016-01-0171112010.1016/j.gsf.2014.12.006Full field reservoir modeling of shale assets using advanced data-driven analyticsSoodabeh Esmaili0Shahab D. Mohaghegh1Asset Development Team, North Operation, California Resources Corporation, California 90024, USAWest Virginia University, 345-E Mineral Resources Bldg., P. O. Box 6070, Morgantown, WV 26506, USAHydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism (sorption process and flow behavior in complex fracture systems - induced or natural) leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called “hard data” directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The “hard data” refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of “soft data” (non-measured, interpretive data such as frac length, width, height and conductivity) in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.http://www.sciencedirect.com/science/article/pii/S1674987114001649Reservoir modelingData driven reservoir modelingTop-down modelingShale reservoirModelingShale |
| spellingShingle | Soodabeh Esmaili Shahab D. Mohaghegh Full field reservoir modeling of shale assets using advanced data-driven analytics Geoscience Frontiers Reservoir modeling Data driven reservoir modeling Top-down modeling Shale reservoir Modeling Shale |
| title | Full field reservoir modeling of shale assets using advanced data-driven analytics |
| title_full | Full field reservoir modeling of shale assets using advanced data-driven analytics |
| title_fullStr | Full field reservoir modeling of shale assets using advanced data-driven analytics |
| title_full_unstemmed | Full field reservoir modeling of shale assets using advanced data-driven analytics |
| title_short | Full field reservoir modeling of shale assets using advanced data-driven analytics |
| title_sort | full field reservoir modeling of shale assets using advanced data driven analytics |
| topic | Reservoir modeling Data driven reservoir modeling Top-down modeling Shale reservoir Modeling Shale |
| url | http://www.sciencedirect.com/science/article/pii/S1674987114001649 |
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