{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T20:26:56Z","timestamp":1778617616571,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A novel workflow is presented for integrating fiber optic Distributed Temperature Sensor (DTS) data in numerical simulation model for the Cyclic Steam Stimulation (CSS) process, using an intelligent optimization routine that automatically learns and improves from experience. As the steam\u2013oil relationship is the main driver for forecasting and decision-making in thermal recovery operations, knowledge of downhole steam distribution across the well over time can optimize injection and production. This study uses actual field data from a CSS operation in a heavy oil field in California, and the value of integrating DTS in the history matching process is illustrated as it allows the steam distribution to be accurately estimated along the entire length of the well. The workflow enables the simultaneous history match of water, oil, and temperature profiles, while capturing the reservoir heterogeneity and the actual physics of the injection process, and ultimately reducing the uncertainty in the predictive models. A novel stepwise grid-refinement approach coupled with an evolutionary optimization algorithm was implemented to improve computational efficiency and predictive accuracy. DTS surveillance also made it possible to detect a thermal communication event due to steam channeling in real-time, and even assess the effectiveness of the remedial workover to resolve it, demonstrating the value of continuous fiber optic monitoring.<\/jats:p>","DOI":"10.3390\/s20113075","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"3075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Integrating Fiber Optic Data in Numerical Reservoir Simulation Using Intelligent Optimization Workflow"],"prefix":"10.3390","volume":"20","author":[{"given":"Giuseppe","family":"Feo","sequence":"first","affiliation":[{"name":"Department of Petroleum Engineering Louisiana State University, Baton Rouge, LA 70803, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6214-9738","authenticated-orcid":false,"given":"Jyotsna","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Petroleum Engineering Louisiana State University, Baton Rouge, LA 70803, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Cunningham","sequence":"additional","affiliation":[{"name":"Vaquero Energy, Santa Maria, CA 93454, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"ref_1","unstructured":"Prats, M. (1982). H.L Doherty Memorial Fund of AIME, Society of Petroleum Engineers of AIME, Thermal Recovery."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"020401","DOI":"10.1063\/1.5144123","article-title":"Big data on the horizon from a new generation of distributed optical fiber sensors","volume":"5","author":"Westbrook","year":"2020","journal-title":"APL Photonics"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shiloh, L., Eyal, A., and Giryes, R. (2018, January 24\u201328). Deep Learning Approach for Processing Fiber-Optic DAS Seismic Data. Proceedings of the 26th International Conference on Optical Fiber Sensors, Lausanne, Switzerland. OSA Technical Digest.","DOI":"10.1364\/OFS.2018.ThE22"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Makarenko, A.V. (2016, January 13\u201316). Deep learning algorithms for signal recognition in long perimeter monitoring distributed fiber optic sensors. Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietri sul Mare, Italy.","DOI":"10.1109\/MLSP.2016.7738863"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Saputelli, L., Mendoza, H., Finol, J., Rojas, L., Lopez, E., Bravo, H., and Buitriago, S. (1999, January 17\u201319). Monitoring Steamflood Performance Through Fiber Optic Temperature Sensing. Proceedings of the International Thermal Operations and Heavy Oil Symposium, Bakersfield, CA, USA.","DOI":"10.2118\/54104-MS"},{"key":"ref_6","unstructured":"Sensing, A.P. (2020, May 27). Available online: https:\/\/www.apsensing.com\/technology\/distributed-acoustic-sensing-das-dvs."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Grattan, K.T., and Meggitt, B.T. (1995). Optical Fiber Sensor Technology, Chapman & Hall.","DOI":"10.1007\/978-94-011-1210-9"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Feo, G., Sharma, J., Kortukov, D., Williams, W., and Ogunsanwo, T. (2020). Distributed Fiber Optic Sensing for Real-Time Monitoring of Gas in Riser during Offshore Drilling. Sensors, 20.","DOI":"10.3390\/s20010267"},{"key":"ref_9","unstructured":"Wang, Z. (2012). The Uses of Distributed Temperature Survey (DTS) Data. [Ph.D. Thesis, Stanford University]."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sanni, M., Hveding, F., Kokal, S., and Zefzafy, I. (2018, January 24\u201326). Lessons Learned from In-well Fiber-optic DAS\/DTS Deployment. Proceedings of the SPE Annual Technical Conference and Exhibition, Dallas, TX, USA.","DOI":"10.2118\/191470-MS"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Angove, T. (1970, January 28\u201330). Optimizing High Temperature Steam Stimulation Operations. Proceedings of the Society of Petroleum Engineers California Regional Meeting, Santa Barbara, CA, USA.","DOI":"10.2523\/3178-MS"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Goiffon, J.J., and Gualtieri, D. (2006, January 11\u201313). Applications of Fiber Optic Real Time Distributed Temperature Sensing (DTS) in A Heavy Oil Production Environment. Proceedings of the Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands.","DOI":"10.2523\/99449-MS"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karaman, O.S., Kutlik, R.L., and Kluth, E.L. (1996, January 22\u201324). A Field Trial to Test Fiber Optic Sensors for Downhole Temperature and Pressure Measurements, West Coalinga Field, California. Proceedings of the SPE Western Regional Meeting, Anchorage, AK, USA.","DOI":"10.2523\/35685-MS"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Beshry, M.A., Krawchuk, P., Brown, G.A., and Brough, B. (2006, January 24\u201327). Predicting the Flow Distribution on total E&P Canada\u2019s Joslyn Project Horizontal SAGD Producing Wells Using Permanently Installed Fiber-Optic Monitoring. Proceedings of the SPE Annual Technical Conference and Exhibition, San Antonio, TX, USA.","DOI":"10.2523\/102159-MS"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Brown, G. (2006, January 24\u201327). Monitoring Multilayered Reservoir Pressure and Gas\/Oil Ratio Changes Over Time Using Permanently Installed Distributed Temperature Measurements. Proceedings of the SPE Annual Technical Conference and Exhibition, San Antonio, TX, USA.","DOI":"10.2523\/101886-MS"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Brown, G., Field, D., Davies, J., Vollins, P., and Garayeva, N. (2005, January 9\u201312). Production Monitoring Through Open-hole Gravel-Pack Completions Using Permanently Installed Fiber-Optic Distributed Temperature Systems in the BP-Operated Azeri Field in Azerbaijan. Proceedings of the SPE 95419 presented at the SPE Annual Technical Conference and Exhibition, Dallas, TX, USA.","DOI":"10.2523\/95419-MS"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ouyang, L.-B., and Belanger, D. (2004, January 26\u201329). Flow profiling by Distributed Temperature Sensor (DTS) System\u2013Expectation and Reality. Proceedings of the SPE Annual Technical Conference and Exhibition, Houston, TX, USA.","DOI":"10.2118\/90541-MS"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Johnson, D.O., Sugianto, R., Mock, P.H., and Jones, C.H. (2004, January 11\u201312). Identification of Steam-Breakthrough Intervals with DTS Technology. Proceedings of the SPE Production and Facilities, Houston, TX, USA.","DOI":"10.2118\/87631-PA"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fryer, V.I., Dong, S., Otsubo, Y., Brown, G.A., and Guilfoyle, P. (2005, January 5\u20137). Monitoring of Real-Time Temperature Profiles Across Multizone Reservoirs during Production and Shut in Periods Using Permanent Fiber-Optic Distributed Temperature Systems. Proceedings of the SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, IN, USA.","DOI":"10.2523\/92962-MS"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rahman, M., Zannitto, P.J., Reed, D.A., and Allan, M.E. (2011, January 19\u201321). Application of Fiber-Optic Distributed Temperature Sensing Technology for Monitoring Injection Profile in Belridge Field, Diatomite Reservoir. Proceedings of the SPE Digital Energy Conference and Exhibition, Woodlands, TX, USA.","DOI":"10.2118\/144116-MS"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shirdel, M., Buell, R.S., Wells, M., Muharam, C., and Sims, J. (2016, January 24\u201329). Horizontal Steam Injection Flow Profiling Using Fiber Optics. Proceedings of the SPE Annual Technical Conference and Exhibition, Dubai, UAE.","DOI":"10.2118\/181431-MS"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, X., Lee, J., Thigpen, B., Vachon, G.P., Poland, S.H., and Norton, D. (2008, January 25\u201327). Modeling Flow Profile Using Distributed Temperature Sensor (DTS) System. Proceedings of the SPE Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands.","DOI":"10.2118\/111790-MS"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, X., Bussear, T.R., and Hasan, A.R. (2010, January 1\u20133). Technique to Improve Flow Profiling Using Distributed-Temperature Sensors. Presented at SPE Latin American and Caribbean Petroleum Engineering Conference, Lima, Peru.","DOI":"10.2118\/138883-MS"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nath, D.K. (2005, January 5\u20137). Fiber Optic Used to Support Reservoir Temperature Surveillance in Duri Steam Flood. Proceedings of the SPE Western Regional Meeting, Jakarta, IN, USA.","DOI":"10.2523\/93240-MS"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nath, D.K., Sugianto, R., and Finely, D. (2005, January 1\u20133). Fiber Optic Distributed Temperature Sensing Technology Used for Reservoir Monitoring in an Indonesian Steamflood. Proceedings of the SPE International Thermal Operations and Heavy Oil Symposium, Calgary, AB, Canada.","DOI":"10.2118\/97912-MS"},{"key":"ref_26","unstructured":"(2020, May 27). CMG Numerical Simulation Package. Available online: https:\/\/www.cmgl.ca\/."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Syrtlanov, V., Golovatskiy, Y., Ishimov, I., and Mezhnova, N. (2019, January 22\u201324). Assisted History Matching for Reservoir Simulation Models. Proceedings of the SPE Russian Petroleum Technology Conference, Moscow, Russia.","DOI":"10.2118\/196878-RU"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hao, C., Wen, X.-H., Milliken, W., and Akhil, D.-G. (2004, January 26\u201329). Field Experiences with Assisted and Automatic History Matching Using Streamline Models. Proceedings of the SPE Annual Technical Conference and Exhibition, Houston, TX, USA.","DOI":"10.2523\/89857-MS"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nghiem, L., Mirzabozorg, A., Yang, C., and Chen, Z. (2013, January 11\u201313). Differential Evolution for Assisted History Matching Process: SAGD Case Study. Proceedings of the SPE Heavy Oil Conference, Calgary, AB, Canada.","DOI":"10.2118\/165491-MS"},{"key":"ref_30","unstructured":"(2020, May 20). CMG History Matching Using CMOST. Available online: https:\/\/www.cmgl.ca\/uploads\/files\/pdf\/SOFTWARE\/2013%20Product%20Sheets\/13-CM-11_CMOST_2013_Overview.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ranazzi, P.H., and Pinto, M.A.S. (2017, January 5\u20138). Assisted history matching using combined optimization methods. Proceedings of the Iberian Latin-American Congress on Computational Methods in Engineering, Florian\u00f3polis, Brazil.","DOI":"10.20906\/CPS\/CILAMCE2017-1042"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3075\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:33:43Z","timestamp":1760175223000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,29]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20113075"],"URL":"https:\/\/doi.org\/10.3390\/s20113075","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,29]]}}}