Real-Time Monitoring and Interpretation of Wireline Formation Testing Using Ensemble Kalman Filter
- Hani Elshahawi (Shell) | Andrey Filippov (Halliburton)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 6-9 May, Houston, Texas
- Publication Date
- Document Type
- Conference Paper
- 2019. Offshore Technology Conference
- 5.5 Reservoir Simulation, 5.5.8 History Matching, 5.6.4 Drillstem/Well Testing, 5.6 Formation Evaluation & Management, 4.3 Flow Assurance, 5.1.5 Geologic Modeling, 5 Reservoir Desciption & Dynamics, 1.6 Drilling Operations, 4 Facilities Design, Construction and Operation
- Optical Density, EnKF, Multi-channel Sensor, OBM Filtrate, Real Time
- 16 in the last 30 days
- 226 since 2007
- Show more detail
- View rights & permissions
The ensemble Kalman filter (EnKF) algorithm is an elegant and effective method to optimize model parameters based on differences with predictions of model and measurement data. Great progress has been accomplished using EnKF for data assimilation within reservoir modeling during the last two decades. A typical example where data assimilation is necessary is history matching—the process of adjusting the model variables to account for observations of rates, pressure, saturations, and other variables. In contrast, much less attention has been given to flow model optimization for other workflows, such as drilling, production, flow assurance, and well testing.
Providing two examples of applying the EnKF for real-time quantification of sensor-generated data is the aim of this paper. These examples include the analysis of the declining production curve and zonal pressure sensor data for evaluating matrix permeabilities and processing the multichannel optical to monitor the cleanup of hydrocarbon fluid samples during formation-tester sampling.
Additionally, how the EnKF algorithm can be successfully applied to segmented multichannel sensor field data obtained from multichannel optical density sensors exhibiting the gradual transition from oil-based mud (OBM) filtrate to native formation fluid during formation-tester sampling stations is discussed. A simple algebraic proxy model is used to predict the decline of the volumetric fraction of OBM filtrate with time during formation-tester sampling.
To implement and test the algorithm, a proof-of-concept MATLAB code was developed. Synthetic (simulated) pressure flow rate data were used for the production decline case while the actual field data from eight channel optical sensors were used for the formation-testing case. Model runs were performed in 50 to 60 combinations of model parameters, which were normally distributed around the best-guess values at the initial step. For both cases, only two to three iterations of the algorithm were sufficient to obtain values of the matching parameters.
|File Size||755 KB||Number of Pages||6|
Aanonsen, S. I, Nævdal, G., Oliver, D. S.. 2009. The Ensemble Kalman Filter in Reservoir Engineering–a Review. SPE Journal 14 (03). SPE-117274-PA. https://doi.org/10.2118/117274-PA.
Elahi, S. H. and Jafarpour, B. 2015. Characterization of Fracture Length and Conductivity From Tracer Test and Production Data With Ensemble Kalman Filter. Presented at the Unconventional Resources Technology Conference, San Antonio, Texas, 20–22 July. URTEC-2174307-MS. https://doi.org/10.15530/URTEC-2015-2174307.
Han, M., Li, G., and Chen, J. 2014. Assimilating Microseismic and Well-Test Data by Use of EnKF for Accurate Reservoir Characterization. SPE Journal 20 (01). SPE-171554-PA. https://doi.org/10.2118/171554-PA.
Kristensen, M., Chugunov, N., Gisolf, A.. 2017. Real-Time Formation Evaluation and Contamination Prediction Through Inversion of Downhole Fluid Sampling Measurements. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 9–11 October. SPE-187432-MS. https://doi.org/10.2118/187432-MS.
Leskens, M., Smeulers, J. P. M., and Gryzlov, A. 2008. Downhole Multiphase Metering In Wells By Means Of Soft-sensing. Presented at the Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands, 25–27 February. SPE-112046-MS. https://doi.org/10.2118/112046-MS.
Malik, M., Torres-Verdin, C., and Sepehrooni, K. 2006. Robust and Efficient Simulation of Formation-Tester Measurements With a Rigorous Compositional Simulation Code. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. SPE-102151-MS. https://doi.org/10.2118/102151-MS.
Zazovsky, A. 2008. Monitoring and Prediction of Cleanup Production During Sampling. Presented at the SPE International Symposium and Exhibition on Formation Damage Control, Lafayette, Louisiana, 13–15 February. SPE-112409-MS. https://doi.org/10.2118/112409-MS.