Use of Tracers to Evaluate and Optimize Scale-Squeeze-Treatment Design in the Norne Field
- Oscar Vazquez (Heriot-Watt University) | Eric Mackay (Heriot-Watt University) | Tore Tjomsland (Statoil) | Ole-Kristian Nygård (Statoil) | Elisabeth Storås (Statoil)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- February 2014
- Document Type
- Journal Paper
- 5 - 13
- 2014.Society of Petroleum Engineers
- 4.3.4 Scale, 4.1.2 Separation and Treating, 5.3.2 Multiphase Flow, 5.6.5 Tracers, 3.3.1 Production Logging, 1.10 Drilling Equipment, 5.5.8 History Matching
- Squeeze, Modeling, Tracer
- 1 in the last 30 days
- 374 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
When squeezing scale inhibitors (SIs) into oil-production wells, the inhibitor should usually be uniformly placed in the open intervals to optimize squeeze lifetime. In wells with varying reservoir quality and/or significant crossflow, however, uniform placement is difficult to obtain. Flow diverters are frequently used to improve the chemical placement. In many cases, it is of great interest to evaluate the squeeze performance and assess the actual placement and back production of inhibitor to gather well information and thereby optimize future squeeze designs. This can be particularly interesting in subsea wells in which other types of data collection, such as production logging, are not feasible because of high intervention costs and high operational risk. This study suggests the use of tracers during squeeze treatments to evaluate the placement as an alternative to running production-logging tools (PLTs). The main purpose of this paper is to demonstrate the applicability of tracers [in this particular study, the injection of a potassium chloride (KCl) slug in a producer well in the Norne Field] to evaluate the layer flow-rate profile along the completion interval, which depends on the pressure and geological properties of each layer. The study consists first of verifying the layer flow-rate profile predicted by a history-matched reservoir model. On the basis of this layer-flow rate profile, a tracer-injection program is designed, which includes two production stages at different rates. Finally, on the basis of the reservoir-model predictions, it is identified that each layer is at different pressures, which leads to a distinctive return profile. To evaluate the match between the observed and the simulation data, the layer flow-rate profile from the reservoir model was used to populate a specialized near-wellbore model for scale-squeeze treatments. The match between the observed data and the simulated data was good. However, the near-wellbore model, in particular the layer flow-rate profile, was fine-tuned further. Finally, the fine-tuned near-wellbore model was used to optimize future treatments more accurately with the fine-tuned layer flow-rate profile.
|File Size||830 KB||Number of Pages||9|
Appelo, C.A.J. and Postma, D. 2006. Geochemistry, Groundwater, and Pollution, second edition (second printing). Rotterdam, The Netherlands: A.A. Balkema.
Cubillos, H., Torgersen, H., Chatzichristos, C., and Lamela, M. 2006. Best Practice and Case Study of Interwell Tracer Program Designs. Presented at the First International Oil Conference and Exhibition, Cancun, Mexico, 31 August–2 September. SPE-103891-MS. http://dx.doi.org/10.2118/103891-MS.
Dalland, A., Worsley, D., and Ofstad, K. 1988. A lithostrategraphic scheme for the Mesozoic and Cenozoic succession offshore mid- and northern Norway. NPD Bulletin No. 4, Oljedirektoratet (Norwegian Petroleum Directorate), Stavanger, Norway (January 1988), http://npd.no/Global/Norsk/3%20-%20Publikasjoner/NPD%20Bulletin/NPD_BulletinNo4.pdf.
Datta-Gupta, A., Lake, L.W., and Pope, G.A. 1995. Characterizing heterogeneous permeable media with spatial statistics and tracer data using sequential simulated annealing. Math. Geol. 27 (6): 763-787. http://dx.doi.org/10.1007/bf02273537.
Du, Y. and Guan, L. 2005. Interwell Tracer Test: Lessons Learned from Past Field Studies. Presented at the SPE Asia Pacific Oil and Gas Conference and Exhibition, Jakarta, 5–7 April. SPE-93140-MS. http://dx.doi.org/10.2118/93140-MS.
Hajizadeh, Y., Demyanov, V., Mohamed, L. et al. 2011. Comparison of Evolutionary and Swarm Intelligence Methods for History Matching and Uncertainty Quantification in Petroleum Reservoir Models. In Intelligent Computational Optimization in Engineering, M. Köppen, G. Schaefer, and A. Abraham, Vol. 366, Chap. 8, 209-240. Studies in Computational Intelligence, Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-21705-0_8.
Huseby, O., Chatzichristos, C., Sagen, J. et al. 2005. Use of natural geochemical tracers to improve reservoir simulation models. J. Pet. Sci. Eng. 48 (3–4): 241-253. http://dx.doi.org/10.1016/j.petrol.2005.06.002.
Huseby, O., Valestrand, R., Naevdal, G., and Sagen, J. 2010. Natural and Conventional Tracers for Improving Reservoir Models Using the EnKF Approach. SPE J. 15 (4): 1047–1061. SPE-121190-PA. http://dx.doi.org/10.2118/121190-PA.
Kennedy, J. and Eberhart, R. 1995. Particle swarm optimisation. In Proceedings of the IEEE International Conference on Neural Networks, Vol. 4, 1942–1948. Piscataway, New Jersey: IEEE Service Center.
McLaughlin, J.S. 1996. Radioactive Tracers: Review of Principle Factors in Design and Application. Presented at the Permian Basin Oil and Gas Recovery Conference, Midland, texas, USA, 27–29 March. SPE-35233-MS. http://dx.doi.org/10.2118/35233-MS.
Mohamed, L., Christie, M., and Demyanov, V. 2010a. Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification. SPE J. 15 (1): 31–38. SPE-119139-PA. http://dx.doi.org/10.2118/119139-PA.
Mohamed, L., Christie, M., and Demyanov, V. 2010b. Reservoir Model History Matching With Particle Swarms: Variants Study. Presented at the SPE Oil and Gas India Conference and Exhibition, Mumbai, India, 20–22 January. SPE-129152-MS. http://dx.doi.org/10.2118/129152-MS.
Onwubolu, G.C. and Babu, B.V. 2004. New Optimization Techniques in Engineering. Berlin: Studies in Fuzziness and Soft Computing, Springer-Verlag.
Tjomsland, T., Sandøy, B., Fadnes, F.H., and McCartney, R. 2012. Application of Multirate Well Tests to Scale Management: Part 2—Interpretation of MRTs With Known Produced-Water Origin. SPE Prod & Oper 27 (3): 327–336. SPE-156519-PA. http://dx.doi.org/10.2118/156519-PA.
Valestrand, R., Sagen, J., Naevdal, G., and Huseby, O. 2010. The Effect of Including Tracer Data in the Ensemble-Kalman-Filter Approach. SPE J. 15 (2): 454–470. SPE-113440-PA. http://dx.doi.org/10.2118/113440-PA.
Vazquez, O., Corne, D., Mackay, E.J., and Jordan, M.M. 2013a. Automatic Isotherm Derivation From Field Data for Oilfield Scale-Inhibitor Squeeze Treatments. SPE J. 18 (3): 563–574. SPE-154954-PA. http://dx.doi.org/10.2118/154954-PA.
Vazquez, O., Mackay, E.J., and Sorbie, K.S. 2006. Development of a Non-Aqueous Scale Inhibitor Squeeze Simulator. Presented at the SPE International Oilfield Scale Symposium, Aberdeen, UK, 30 May–1 June. SPE-100521-MS. http://dx.doi.org/10.2118/100521-MS.
Vazquez, O., McCartney, R., and Mackay. E. 2013b. Produced-Water-Chemistry History Matching Using a 1D Reactive Injector/Producer Reservoir Model. SPE Prod & Oper 28 (4): 369–375. SPE-164113-PA. http://dx.doi.org/10.2118/164113-PA.
Zhang, H.R. and Sorbie, K.S. 1997. SQUEEZE V User's Manual. Edinburgh, UK: Department of Petroleum Engineering, Heriot-Watt University.