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Video: Formation Fluid Sampling Simulation: The Key to Successful Job Design and Post-Job Performance Evaluation

Authors
Morten Kristensen (Schlumberger) | Hadrien Dumont (Schlumberger) | Tunde Akindipe (ConocoPhillips) | Nikita Chugunov (Schlumberger) | German Garcia (Schlumberger)
Document ID
SPE-195836-PT
Publisher
Society of Petroleum Engineers
Publication Date
2019
Document Type
Presentation
Language
English
Copyright
2019. Copyright is retained by the author. This presentation is distributed by SPE with the permission of the author. Contact the author for permission to use material from this video.
Keywords
Proxy modeling, Reservoir simulation, Machine learning, Formation testing, Downhole fluid sampling
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Acquisition of fluid samples using wireline formation testers (WFTs) is an integral part of reservoir evaluation and fluid characterization. The increasing complexity of fluid sampling operations, especially in remote or offshore fields, requires a careful planning process involving systematic de-risking of the sampling objectives through quantitative evaluation of sampling hardware performance under uncertain downhole conditions and reservoir properties. During job execution, the cleanup of mud filtrate is monitored using downhole fluid analysis (DFA) sensor measurements. In addition to quantifying produced contamination and providing guidance for real-time decisions, these measurements hold valuable information about formation and fluid properties that can be extracted through advanced interpretation workflows.

In this paper, we demonstrate how a quantitative, model-based workflow was applied to both planning and interpretation for a series of sampling jobs in a remote and harsh environment. At its core, the workflow consists of high-resolution numerical flow models for the filtrate cleanup process that cover both conventional and focused sampling tools. To enable real-time, interactive, and probabilistic workflows, we use machine learning techniques to construct fast, high-fidelity proxy models, which, after thorough validation, replace numerical simulation in the workflow. Finally, the workflow employs methods for uncertainty quantification, global sensitivity analysis, and model inversion.

During the pre-job planning phase, the model-based workflow was used to select and mobilize the optimal sampling hardware, estimate sampling time uncertainty, and pinpoint the dominant sources of this uncertainty through global sensitivity analysis. After successful sample acquisition, the DFA measurements were reconciled with the cleanup model and the petrophysical evaluation to extract additional value from the measurements. Using measurements of water-cut and pressure, and conditioned to the petrophysical evaluation, the cleanup model was inverted for two-phase relative permeabilities. This recently developed methodology complements laboratory measurements of relative permeability on core samples.

Building on previous work in this area, this paper demonstrates the practical application of advanced planning and interpretation workflows for downhole fluid sampling. The methodology presented couples traditional, full-physics flow modeling with modern machine learning techniques to achieve highly agile workflows, enabling operators to more efficiently plan sampling jobs and extract value from the measurements.

Aleman, D.O. , Kristensen, M. & Chugunov, N. . (2019). Deep Learning and Markov Chain Monte Carlo (MCMC) for Planning and Interpretation of Downhole Fluid Sampling. Paper SPE 195800 presented at the SPE Annual Technical Conference and Exhibition, Calgary, Canada, 30 September – 2 October. Cig, K. , Ayan, C. , Kristensen, M , Mackay, E. , & Elbekshi, A . (2014). A Novel Methodology for Estimation of Multiphase Flow Properties from Sampling Data of Wireline Formation Tester. Paper SPE 170648 presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, The Netherlands, 27 – 29 October. DOI: 10.2118/170648-PA. Kristensen, M. , Chugunov, N. , Cig, K. , & Jackson, R . (2018). Proxy-Enabled Stochastic Interpretation of Downhole Fluid Sampling Under Immiscible Flow Conditions. Petrophysics, 59(5), 633–648. Kristensen, M. , Chugunov, N. , Gisolf, A. , Biagi, M. , & Dubost, F. . (2019). Real-Time Formation Evaluation and Contamination Prediction Through Inversion of Downhole Fluid Sampling Measurements. SPE Res. Eval. & Eng, 22(2). Liang, L. , Abubakar, A. , & Habashy, T. . (2011). Estimating Petrophysical Parameters and Average Mud-Filtrate Invasion Rates Using Joint Inversion of Induction Logging and Pressure Transient Data. Geophysics, 76(2), E21–E34. DOI: 10.1190/1.3541963. Ramakrishnan T.S. & Wilkinson, D.J . (1999). Water-Cut and Fractional Flow Logs from Array Induction Measurements. SPE Res. Eval. & Eng, 2(1). DOI: 10.2118/54673-PA.

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SPE

Formation Fluid Sampling Simulation: The Key to Successful Job Design and Post-Job Performance Evaluation

Kristensen, Morten, Schlumberger
Dumont, Hadrien, Schlumberger
Akindipe, Tunde, ConocoPhillips
Chugunov, Nikita, Schlumberger
Garcia, German, Schlumberger
195836-MS SPE Conference Paper - 2019
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