As the development of shale oil and gas becomes increasingly significant, so does the need for modeling tools for their accurate and timely forecasting. The basic question then arises: Are the simulation tools that we have suitable for the job? Can they explain, and ultimately forecast, the contribution to flow from natural or hydraulically induced fractures? Ultimately, we need to be confident that the results from such models are sufficiently reliable for decision-making purposes. As Alexandre Emerick stated in JPT (April 2014), “The goal is to generate models for production forecasting aiding the decision-making process involved in the development and management of petroleum reservoirs.”
The ability to model this increasingly important asset class is one of the primary issues facing reservoir and simulation engineers today. Our existing arsenal of simulation tools is certainly time-tested for conventional assets, but can they deliver reliable forecasts for this emergent class? Recent literature is rich with relevant studies ranging from fundamental laboratory experiments to entirely empirical, data-driven, approaches for predictive reservoir management. These articles raise numerous questions, including the possible oversimplification of a complex problem, the very nature of the production mechanisms, and the role played by any pre-existing discrete fracture networks.
Additional concerns relating to accuracy and reliability of the underlying data used to populate such models have also been raised. Keeping an open mind on this topic is, I feel, appropriate. One needs to balance the very real need for such predictive tools today with the recognition that future evidence may disrupt certain preconceptions. To this end, four articles have been selected that span a spectrum of ideas. One considers practical applications and solutions, while another covers the emerging technique of molecular simulation. Another eloquently probes various notions in our current state of understanding of shale assets and examines issues in interpreting the forecasts furnished by our simulation models. The fourth article demonstrates some creative thinking by representing a planar fracture by use of a proxy comprising a coupled flowing network model, a concept that may prove flexible and warrants further investigation.
Conventional modeling tools used to predict flow in tight, fractured shales (e.g., dual-porosity models, local grid refinements, tartan grids) may, indeed, provide reliable forecasting. Analytic and data-driven approaches are also acceptable in some circumstances. However, as our knowledge in exploiting these assets deepens with new theory, laboratory experiments, and field and operational experience, we should be prepared to reconsider some of today’s axioms in the future.
This Month's Technical Papers
Recommended Additional Reading
OTC 24913 Improved Numerical Simulation for Shale Gas Reservoirs by Chaohua Guo, Missouri University of Science and Technology, et al.
SPE 164528 Analytical and Numerical Solutions for Multiple Matrix in Fractured Reservoirs: Application to Conventional and Unconventional Reservoirs by Mehmet A. Torcuk, Colorado School of Mines, et al.
SPE 166375 Experimental and Numerical Analysis of Gas Transport in Shale Including the Role of Sorption by K.R. Alnoaimi, Stanford University, et al.
SPE 165409 Streamline-Based Coupled Geomechanics and Reservoir Simulation for Hydromechanical Modeling of CO2 Storage in Saline Aquifers by Behrooz Koohmareh Hosseini, University of Alberta, et al.
|William Bailey, SPE, works at Schlumberger-Doll Research as a principal engineer with 25 years of industry experience. His main technical interests include reservoir engineering, modeling of unconventional assets, multiphase flow in conduits, equipment-failure analysis, and computationally expensive optimization problems including coupled full-field assets. Bailey holds M.Eng. (Hons.) and PhD degrees in petroleum engineering from Imperial College, London, and the Norwegian University of Science and Technology, respectively, and an MBA degree from Warwick Business School. He was chairperson of the SPE New York and New England section and review chairperson for SPE Production & Operations, and he received the inaugural SPE A Peer Apart award. Bailey reviews for various SPE journals and serves on the JPT Editorial Committee and the SPE Books Committee.|
William Bailey, SPE, Principal Engineer, Schlumberger-Doll Research
01 July 2014
Fighting Water With Water: How Engineers Are Turning the Tides on Frac Hits
If the shale sector’s most complex problem can be solved, it will require companies to use their wells as a team. Newly detailed field work shows that a good defense is the key to success.
BP, Chevron Bring Two More Offshore Projects on Stream
Majors BP and Chevron have overcome development challenges and delays to launch their respective Clair Ridge and Big Foot projects.
Simulation Algorithm Benefits by Connecting Geostatistics With Unsupervised Learning
A new geostatistics modeling methodology that connects geostatistics and machine-learning methodologies, uses nonlinear topological mapping to reduce the original high-dimensional data space, and uses unsupervised-learning algorithms to bypass problems with supervised-learning algorithms.
Don't miss out on the latest technology delivered to your email weekly. Sign up for the JPT newsletter. If you are not logged in, you will receive a confirmation email that you will need to click on to confirm you want to receive the newsletter.
07 January 2019
07 January 2019
14 January 2019
09 January 2019
10 January 2019