Background

To choose the right recovery strategy and optimize its field-scale efficiency, reservoir engineers need to develop a qualitative understanding of the reservoir, explore alternative well placements, and consider different techniques for mobilizing immobile oil and improving sweep efficiency, etc. Cause and effect knowledge for the field as a whole is built by iteratively perturbing model parameters, but the ability to explore different scenarios is often limited by the high computational cost of multiphase simulations and not much rigorous work has been done on field management optimization.

There is a strong need for efficient and intutive prescreening and post-processing tools to complement advanced multiphase simulations in assessing the effect of static and dynamic heterogeneity and uncertainty in reservoir description, explore field-scale performance of alternative recovery strategies, and generally provide better decision support to optimize development and production plans.

Flow diagnostics

Flow diagnostics refers to simplified numerical simulations a reservoir engineer can use to quickly locate regions likely to remain unswept, understand how injectors and producers communicate, estimate sweep and displacement efficiency, compute allocation factors, and generally assess the effect of heterogeneity and uncertainty. Flow diagnostics give a set of visually intuitive quantities that:

• give the travel time for mass-less particles that passively follow the flow field from an injector into the reservoir and from a point in the reservoir to the nearest producer;
• delineate regions drained by given producers or swept (flooded) by given injectors;
• determine whether pairs of injectors and producers communicate or not and measure the relative strength of their connection;
• determine how flux is allocated between different injectors and producers;
• establish the volumetric region influenced by specific well-pairs;
• give the residence-time distribution of all flow paths connecting pairs of injectors and producers;
• measure the dynamic heterogeneity within drainage, sweep, or well-pair regions.

All these quantities are quick to compute and can thus be used interactively to explore fluid communication in a geological model before or after more comprehensive multiphase flow simulations.

Use of flow diagnostics to prescreen and post-process accurate reservoir models, or ensembles of such models, will make better use of dynamic simulations and reduce the turnaround time for modeling workflows, e.g., by reducing the need for upscaling, reducing ensemble sizes, improving selection of representative sector models, etc. Flow diagnostics can also be used as surrogate models providing recovery factors and economic quantities like net present value.

Preprocessing		Postprocessing
Computing flow diagnostics is orders of magnitude less expensive than full multiphase simulations. Various flow diagnostics quantities have also shown to correlate well with recovery factors and net present value estimates computed by multiphase simulations. Flow diagnostics are therefore very useful for prescreening models to gain insight into fluid communication and how this affected by heterogeneity and well placement, to rank and compare model ensembles and identify outliers, or to derive preliminary estimates of model uncertainty before running expensive multiphase simulations. You can read more about flow diagnostics as a preprocessing tool in the following user guide:		Understanding the internal fluid communication in a reservoir from well curves and pressure and saturation values alone can often by challenging. Flow diagnostics offers a series of quantities that can be used for postprocessing and enhanced visualization including volumetric quantities like influence regions for individual wells, drainage, sweep, and well-pair regions; interwell connectivity and well-allocation factors; and measures of the dynamic heterogeneity of flow paths (F-Phi plots, Lorenz coefficients, sweep efficiency, etc.) You can read more about flow diagnostics as a postprocessing tool in the following user guide:
User Guide to Flow Diagnostics in MRST - Flow Diagnostics Preprocessors for Model Ensembles. hdl.handle.net/11250/2687239		User Guide to Flow Diagnostics Postprocessing - Simulations in MRST and ECLIPSE Output Format. hdl.handle.net/11250/2687032

Gallery (click on images for larger view)
Ranking of one hundred equiprobable petrophysical realizations of the Norne field model, shown as a cross plot of sweep efficiency versus Lorenz coefficient. The best performance is expected for high sweep efficiency and low Lorenz coefficients (i.e., smaller variation in flow path lengths). The right column shows residence-time distributions and derived oil rate, both as function of dimensionless time.		Screenshot from the postprocessing GUI showing the influence region for injector BR-I-2 of the Brugge benchmark case (simulated with ECLIPSE). The bar chart shows cumulative well allocation factors, from the bottom to the top perforation, for all the producers supported by this injector. The lower-right plot shows bottom-hole pressures as function of time for these injectors.
Sweep regions for six equiprobable realizations from the Egg ensemble model. The geology consists of a series of high-permeability channels on a lower-permeability background. The sweep efficiency will therefore depend largely upon how injectors and producers are placed relative to the channels, and one can generally expect the best recovery when sweep regions are of the same size for all injectors.		Time-dependent flow diagnostics for the Brugge field. The large plot shows how the sweep regions develop over a four-year period. Cells with bright colors are part of the same sweep region over the whole period, whereas grayish colors signify cells that are associated with different sweep regions over the time interval. The bar plot shows how the Lorenz coefficient for the whole reservoir develops over time.
Postprocessing for a black-oil simulation of the Norne field with focus on producer E-1H. The upper-left plot shows the instant fluid distribution as a function of travel time from the producer, the upper-middle plot shows the instant volumetrics inside the drainage zone (right-lower plot), where the upper-right plot shows the well response over the full simulation history.		Pre-processing to determine volumetric connections: Well pairs in communication shown as solid lines, colored by producer and percentage refering to flux allocation. Pie charts show total rate allocation for each well, whereas the graphs show cumulative rate allocation for individual well completions.

Additional literature

1. O. Møyner, S. Krogstad, and K.-A. Lie. The application of flow diagnostics for reservoir management. SPE J., Vol. 20, No. 2, pp. 306-323, 2015. DOI: 10.2118/171557-PA
2. K.-A. Lie. An Introduction to Reservoir Simulation Using MATLAB/GNU Octave: User Guide for the MATLAB Reservoir Simulation Toolbox (MRST). Chapter 13: Flow Diagnostics, pp. 477 - 517, Cambridge University Press, 2019. DOI: https://doi.org/10.1017/9781108591416.018
3. F. Watson, S. Krogstad, K.-A. Lie. Flow diagnostics for model ensembles. ECMOR XVII - 17th European Conference on the Mathematics of Oil Recovery, 2020. DOI: 10.3997/2214-4609.202035133
4. S. Krogstad and H.M. Nilsen. Efficient adjoint-based well-placement optimization using flow diagnostics proxies. ECMOR XVII - 17th European Conference on the Mathematics of Oil Recovery, 2020. DOI: 10.3997/2214-4609.202035227.
5. M. Borregales, O. Møyner, S. Krogstad, K.-A. Lie. Data-driven models based on flow diagnostics. ECMOR XVII - 17th European Conference on the Mathematics of Oil Recovery, 2020. DOI: 10.3997/2214-4609.202035122
6. K.-A. Lie, S. Krogstad, O. Møyner. Application of flow diagnostics and multiscale methods for reservoir management. 2015 Reservoir Simulation Symposium, Houston, Texas, USA, 23-25 February 2015. DOI: 10.2118/173306-MS
7. A. F. Rasmussen and K.-A. Lie. Discretization of flow diagnostics on stratigraphic and unstructured grids. ECMOR XIV, Catania, Sicily, Italy, 8-11 September 2014. DOI: 10.3997/2214-4609.20141844
8. J. R. Natvig and K.-A. Lie. Fast computation of multiphase flow in porous media by implicit discontinuous Galerkin schemes with optimal ordering of elements. J. Comput. Phys, Vol. 227, Issue 24, pp. 10108-10124, 2008. Doi:  10.1016/j.jcp.2008.08.024.
9. B. Eikemo, K.-A. Lie, H.K. Dahle, and G.T. Eigestad. A discontinuous Galerkin method for transport in fractured media using unstructured triangular grids. Adv. Water Resour. Vol. 32, Issue 4, pp. 493-506. 2009. Doi: 10.1016/j.advwatres.2008.12.010.
10. J. R. Natvig, K.-A. Lie, B. Eikemo, and I. Berre. An efficient discontinuous Galerkin method for advective transport in porous media. Adv. Water Resour, Vol. 30, Issue 12, pp. 2424-2438, 2007. Doi: 10.1016/j.advwatres.2007.05.015
11. M. Shahvali, B. Mallison, K. Wei, and H. Gross. An alternative to streamlines for flow diagnostics on structured and unstructured grids. SPE Journal, Vol. 17, No.  3, September 2012, pp. 768-778. Doi: 10.2118/146446-PA
12. G. M. Shook and K. M. Mitchell. A robust measure of heterogenity for ranking earth models: the F Phi curve and dynamics Lorenz coefficient. Paper SPE 124625. SPE Annual Technical Conference and Exhibition, New Orleans, 4-7 October, 2009. Doi: 10.2118/12465-MS.

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This module is included with MRST from version 2012b and onwards under the name 'diagnostics'.