Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits
The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/7/2927 |
| _version_ | 1827286938334265344 |
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| author | Guoxiang Liu Xiongjun Wu Vyacheslav Romanov |
| author_facet | Guoxiang Liu Xiongjun Wu Vyacheslav Romanov |
| author_sort | Guoxiang Liu |
| collection | DOAJ |
| description | The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability. |
| first_indexed | 2024-04-24T10:48:26Z |
| format | Article |
| id | doaj.art-079a3c325d40408e8b882e67d84bc12e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:48:26Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-079a3c325d40408e8b882e67d84bc12e2024-04-12T13:15:12ZengMDPI AGApplied Sciences2076-34172024-03-01147292710.3390/app14072927Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture HitsGuoxiang Liu0Xiongjun Wu1Vyacheslav Romanov2U.S. Department of Energy, National Energy Technology Laboratory, 626 Cochran Mill Rd., Pittsburgh, PA 15236, USAU.S. Army DEVCOM Army Research Laboratory, 6300 Rodman Rd., Aberdeen Proving Ground, MD 21005, USAU.S. Department of Energy, National Energy Technology Laboratory, 626 Cochran Mill Rd., Pittsburgh, PA 15236, USAThe primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.https://www.mdpi.com/2076-3417/14/7/2927hydraulic fracturingmachine learningprobabilityunconventional wells |
| spellingShingle | Guoxiang Liu Xiongjun Wu Vyacheslav Romanov Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits Applied Sciences hydraulic fracturing machine learning probability unconventional wells |
| title | Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits |
| title_full | Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits |
| title_fullStr | Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits |
| title_full_unstemmed | Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits |
| title_short | Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits |
| title_sort | unconventional wells interference supervised machine learning for detecting fracture hits |
| topic | hydraulic fracturing machine learning probability unconventional wells |
| url | https://www.mdpi.com/2076-3417/14/7/2927 |
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