Significant heterogeneity in production rates from coalbed methane (CBM) wells is observed throughout the Surat and Bowen Basins in Eastern Queensland, Australia. Typically, individual well performance predictions are conducted using deterministic coal properties obtained from core data in combination with permeability and pressure measurements from multi-seam well testing. Due to the heterogeneous nature of the coal, the total permeability measured across the tested interval in a single well will likely not represent the behaviour of even the closest of neighbouring wells. This leads to challenges in mapping permeability between and away from wells, and producing representative predictions for field development. A new method is proposed to take a statistically significant population of multi-seam CBM permeability test results, correct to equivalent expected single seam test results and, using the full dataset, relate those results to a probabilistic single well performance envelope for a defined area with similar geological properties and setting. Depth, gas content, saturation, total net coal isopachs and any unique structural features are the primary control points for defining these ‘geodomains’. The resultant single well performance envelopes remove the need to subjectively choose an average permeability to use for each well or coal measure. The results have been demonstrated to reasonably represent the range of observed actual peak water and gas productivities across a variety of geological areas in the Surat and Bowen basins. This provides a method by which discrete representations of well families can be made for a particular geologic setting; leading to an understanding of the range of potential well outcomes which cannot be achieved using a typical deterministic approach.

No abstract

In reviewing the long list of papers this year, it has become apparent to me that the hot topic in reservoir simulation these days is the application of data analytics or machine learning to numerical simulation and with it quite often the promise of data-driven work flows—code for needing to think about the physics less. Data-driven work flows have their place, especially when we have a lot of data and the system is very complex. I’m thinking shales especially, but seeing it being applied to more conventional reservoirs gave me a moment of pause. I can’t help but think that, in terms of the hype cycle as related to the application of machine learning to numerical simulation, we may be approaching the peak of inflated expectations. I say “approaching,” because many companies appear to be dipping their toes in the water, perhaps because they think they should, but few are truly committing to it. Many vocal champions of the approach exist, but most decision-makers just don’t understand it yet. If we cannot explain how something works simply, then thoughtful leaders will tend not to trust it. Whether it be numerical-simulation findings or self-organizing neural networks, the need will always exist for a deep understanding and clarity of explanation of both the discipline and method used. To decision-makers, it will be an attractive concept, but they will generally ask to validate against more traditional methods. I look forward to a future when we are through the trough of disillusionment and start climbing the slope of enlightenment to a new level of productivity. I suspect, though, that it will take at least another 5 years, as our current crop of knowledgeable evangelists become decision-makers themselves and can put in place work flows and teams to leverage the approach appropriately for their problems, intelligently leveraging their years of hard-won experience. I will lay a wager with you, though, that when that time comes, those new ways of working more efficiently will rely just as much if not more upon a deep understanding of reservoir engineering as our current methods. I hope you enjoy these papers, which include examples of both the new approach as well as tried-and-true approaches. Recommended additional reading at OnePetro: www.onepetro.org. SPE 202436 - Fast Modeling of Gas Reservoirs Using Proper Orthogonal Decomposition/Radial Basis Function (POD/RBF) Nonintrusive Reduced-Order Modeling by Jemimah-Sandra Samuel, Imperial College London, et al. IPTC 21417 - A New Methodology for Calculating Wellbore Shut-In Pressure in Numerical Reservoir Simulations by Babatope Kayode, Saudi Aramco, et al. SPE 201658 - Mechanistic Model Validation of Decline Curve Analysis for Unconventional Reservoirs by Mikhail Gorditsa, Texas A&M University, et al.

The Dynamic modeling of induced hydraulic fractures in finite-difference reservoir simulation has historically been a complex, time-consuming and error-prone process that is unsuited to practical application and difficult to reconcile with corresponding analytical solutions.The methodology and workflows presented in this paper address the above problems and provide a process whereby the pressure-rate response of hydraulically fractured wells may be practically and reasonably accurately modeled in coarse-grid reservoir simulation models without recourse to the high computing-overhead methods commonly employed.The correlations and methodologies presented here have been developed by describing fracture behaviour as a function of fracture half length, grid block size and dimensionless fracture conductivity, and 'history-matching' simulated well performance against accepted analytical and simulated responses for a wide range of reservoir and fracture properties. The methodology is applicable to multiple well scenarios (e.g. vertical, partial block fracture penetrations, block aspect ratios not equal to unity, horizontal wells as well as multi-stage hydraulic fractures in single grid blocks), does not require fracture completion information prior to grid construction, and can be implemented in the recurrent section of a simulation project.

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.