Produced-Water-Chemistry History Matching in the Janice Field
- Oscar Vazquez (Heriot Watt University) | Callum Young (Maersk Oil) | Vasily Demyanov (Heriot-Watt University) | Dan Arnold (Heriot-Watt University) | Andrew Fisher (Maersk Oil) | Alasdair MacMillan (Maersk Oil) | Michael Christie (Heriot-Watt University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- November 2015
- Document Type
- Journal Paper
- 564 - 576
- 2015.Society of Petroleum Engineers
- history matching, Produced water chemistry, Tracers
- 1 in the last 30 days
- 296 since 2007
- Show more detail
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Produced-water-chemistry (PWC) data are the main sources of information to monitor scale precipitation in oilfield operations. Chloride concentration is used to evaluate the seawater fraction of the total produced water per producing well and is included as an extra history-matching constraint to reevaluate a good conventionally history-matched (HM) reservoir model for the Janice field. Generally, PWC is not included in conventional history matching, and this approach shows the value of considering the nature of the seawater-injection front and the associated brine mixing between the distinctive formation water and injected seawater.
Adding the extra constraint resulted in the reconceptualization of the reservoir geology between a key injector and two producers. The transmissibility of a shale layer is locally modified within a range of geologically consistent values. Also, a major lineament is identified which is interpreted as a northwest/southeast-trending fault, whereby the zero transmissibility of a secondary shale in the Middle Fulmar is locally adjusted to allow crossflow. Both uncertainties are consistent with the complex faulting known to exist in the region of the targeted wells. Other uncertainties that were carried forward to the assisted-history-matching phase included water allocation to the major seawater injectors; thermal fracture orientation of injectors; and the vertical and horizontal permeability ratio (Kv/Kh) of the Fulmar formation.
Finally, a stochastic particle-swarm-optimization (PSO) algorithm is used to generate an ensemble of HM models with seawater fraction as an extra constraint in the misfit definition. Use of additional data in history matching has improved the original good HM solution. Field oil-production rate is interpreted as improved over a key period, and although no obvious improvement was observed in field water-production rate, seawater fraction in a number of wells was improved.
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Arnold, D., Vazquez, O., Demyanov, V. et al. 2012. Use of Water Chemistry Data in History Matching of a Reservoir Model. Presented at the EAGE Annual Conference and Exhibition in Copenhagen, Denmark, 4–7 June. SPE-154471-MS. http://dx.doi.org/10.2118/154471-MS.
Braden, J. C. and McLelland, W. G. 1993. Produced Water Chemistry Points to Damage Mechanisms Associated With Seawater Injection. Presented at the SPE Western Regional Meeting, Anchorage, 26–28 May. SPE-26045-MS. http://dx.doi.org/10.2118/26045-MS.
Carbone, L. C., Fleming, N., Spark, S. et al. 1999. Scale Management Through Laboratory Analysis and Wellbore Monitoring Surveys. Presented at the SPE European Formation Damage Conference, The Hague, 31 May–1 June. SPE-54733-MS. http://dx.doi.org/10.2118/54733-MS.
Cheng, H., Shook, G. M., Malik, T. et al. 2012. Interwell Tracer Tests To Optimize Operating Conditions for a Surfactant Field Trial: Design, Evaluation, and Implications. SPE Res Eval & Eng 15 (2): 229–242. SPE-144899-PA. http://dx.doi.org/10.2118/144899-PA.
Floris, F. J. T., Bush, M. D., Cuypers, M. et al. 2001. Methods for Quantifying the Uncertainty of the Production Forecasts: A Comparative Study. Petroleum Geosciences 7: S87–S96.
Hajizadeh, Y., Christie, M., and Demyanov, V. 2010. Comparative Study of Novel Population-Based Optimization Algorithms for History Matching and Uncertainty Quantification: PUNQ-S3 Revisited. Prepared for the Abu Dhabi International Petroleum Exhibition and Conference, 1–4 November.
Huseby, O. Chatzichristos, C., Sagen, J. et al. 2005. Use of Natural Geochemical Tracers To Improve Reservoir Simulation Models. J. Petroleum Science and Engineering 48 (3–4): 241–253. http://dx.doi.org/10.1016/j.petrol.2005.06.002.
Ishkov, O., Mackay, E., and Sorbie, K. 2009. Reacting Ions Method to Identify Injected Water Fraction in Produced Brine. Presented at the SPE International Symposium on Oilfield Chemistry, The Woodlands, Texas, USA, 20–22 April. SPE-121701-MS. http://dx.doi.org/10.2118/121701-MS.
Johnson, H. D. and Stewart, D. J. 1985. The Role of Clastic Sedimentology in the Exploration and Production of Oil and Gas in the North Sea, In Sedimentology: Recent Developments and Applied Aspects, 249–310. Oxford: Blackwell Scientific Publications.
Kazemi, A., Stephen, K. D., and Shams, A. 2011. Seismic History Matching of Nelson Using Time-Lapse Seismic Data: An Investigation of 4D Signature Normalization. SPE Res Eval & Eng 14 (5): 621–633. SPE-131538-PA. http://dx.doi.org/10.2118/131538-PA.
Kelley, C. T. 1999. Iterative Methods for Optimization. Philadelphia, Pennsylvania: Society for Industrial and Applied Mathematics.
Libbey, R. B., Williams-Jones, A. E., Melosh, B. L. et al. 2015. Characterization of Geothermal Activity Along the North American–Caribbean Plate Boundary in Guatemala: The Joaquina Geothermal Field. Geothermics 56: 17–34. http://dx.doi.org/10.1016/j.geothermics.2015.03.002.
Mackay, E. J., Jordan, M. M., Feasey, N. D. et al. 2005. Integrated Risk Analysis for Scale Management in Deepwater Developments. SPE Prod Fac 20 (2): 138–154. SPE-87459-PA. http://dx.doi.org/10.2118/87459-PA.
Mohamed, L., Christie, M., and Demyanov, V. 2009. Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, USA, 2–4 February. SPE-119139-MS. http://dx.doi.org/10.2118/119139-MS.
Mohamed, L., Christie, M., and Demyanov, V. 2010. Reservoir Model History Matching With Particle Swarms: Variants Study. Presented at the SPE Oil and Gas India Conference and Exhibition, Mumbai, 20–22 January. SPE-129152-MS. http://dx.doi.org/10.2118/129152-MS.
Puntervold, T. and Austad, T. 2007. Injection of Seawater and Mixtures With Produced Water Into North Sea Chalk Formation: Impact on Wettability, Scale Formation, and Rock Mechanics Caused by Fluid-Rock Interaction. Presented at the SPE/EAGE Reservoir Characterization and Simulation Conference, Abu Dhabi, 28–31 October. SPE-111237-MS. http://dx.doi.org/10.2118/111237-MS.
Sato, T., Ohsato, K., Shiga, T. et al. 2003. A Study of Reservoir Estimation for a Deep-Seated Geothermal Reservoir Using TOUGH2 and CHEMITOUGH2. In Proc. of TOUGH Symposium 2003. Berkeley, California, USA: Lawrence Berkeley National Laboratory.
Scheck, M. and Ross, G. 2008. Improvement of Scale Management Using Analytical and Statistical Tools. Presented at the SPE International Oilfield Scale Conference, Aberdeen, 28–29 May. SPE-114103-MS. http://dx.doi.org/10.2118/114103-MS.
Smalley, P. C., Råheim, A., Dickson, J. A. D. et al. 1988. 87Sr/86Sr in Waters From the Lincolnshire Limestone Aquifer, England, and the Potential of Natural Strontium Isotopes as a Tracer for a Secondary Recovery Seawater Injection Process in Oilfields. Appl. Geochem. 3 (6): 591–600. http://dx.doi.org/10.1016/08832927(88)90091-1.
Stephen, K. D., Shams, A., and MacBeth, C. 2009. Faster Seismic History Matching in a United Kingdom Continental Shelf Reservoir. SPE Res Eval & Eng 12 (4): 586–594. SPE-107147-PA. http://dx.doi.org/10.2118/107147-PA.
Vazquez, O., Young, C., Demyanov, V. et al. 2013. Estimating Scale Deposition Through Reservoir History Matching in the Janice Field. SPE Prod & Oper 29 (1): 21–28. SPE-164112-PA. http://dx.doi.org/10.2118/164112-PA.
Young, C. 2012. Produced Water Chemistry History Matching of a North Sea Field. MSc thesis, Institute of Petroleum Engineering, Heriot-Watt University.