As the world struggles through the COVID-19 crisis and our industry suffers from linked oversupply and demand reduction, we are all forced to refocus on what makes a difference to the bottom line.

In the numerical reservoir simulation space, that is generally, “How do we answer a decision-related question in the least amount of time with an acceptable degree of confidence?”

Simulation has always been a double-edged sword—a method that, when well-used in fit-for-purpose ways to answer specific questions, can deliver real value. Conversely, when it is used as a substitute for understanding, perhaps to justify a development decision or simply to convince ourselves that we understand a system far better than we really do, many staff years of effort can be quickly lost while not delivering very much.

Fit-for-purpose approaches likely will be of ever-increasing focus going ­forward. If it is not adding value, it should not be done. But “fit for purpose” encompasses a wide range of possibilities—leveraging new approaches as well as learning from old approaches and improving current approaches. It is these three prongs that have guided my paper selections for this edition.

While the rapid rise to prominence of methods that eschew conventional numerical modeling approaches in favor of data-driven proxy approaches provides us with some new tools to answer these questions, these tools are unlikely to be reliably predictive unless they incorporate governing physics. It is for this reason that one of the following papers spells out just such a solution, marrying fundamental material-balance governing equations with analytics-driven clustering techniques to deliver what appears to be a fit-for-purpose approach to a complex problem.

Keeping in mind the quote attributed to George Santayana—“Those who cannot learn from history are doomed to repeat it”—the second paper is an interesting look back at different attempts to simulated unconventional plays, while the third paper is an interesting extension of a current approach to well deconvolution.

I hope you enjoy reading this selection of papers and look forward to what the future may hold in the numerical simulation space.

This Month's Technical Papers

Spatiotemporal Clustering-Based Formulation Aids Multiscale Modeling

Learnings Applied to Reservoir Simulation of Unconventional Plays

Incorporating Constraints Improves Least-Squares Multiwell Deconvolution

Recommended Additional Reading

SPE 195252 On the Application of a Simplified Temperature-Dependent Friction-Theory Viscosity Model in Compositional and Thermal-Compositional Reservoir Simulations by Hussein Alboudwarej, Chevron, et al.

SPE 196213 Productivity Decline: Improved Production Forecasting Through Accurate Representation of Well Damage by Yan Li, Chevron, et al.

SPE 197331 Understanding the Performance and Application Window of Autonomous Inflow-Control Devices by Oscar Becerra Moreno, Baker Hughes, et al.

Mark Burgoyne, SPE, is a principal reservoir engineer for Santos. He has more than 25 years of industry experience, including technical leadership and subsurface management roles with Santos and hydraulic-fracturing, cementing, and coiled-tubing roles with Schlumberger. Burgoyne holds a bachelor’s degree in chemical engineering, a master’s degree in petroleum engineering, and an MBA degree.


Mark Burgoyne, SPE, Principal Reservoir Engineer, Santos

01 July 2020

Volume: 72 | Issue: 7



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