Applying Innovative Production Modeling Techniques to Quantify Fracture Characteristics, Reservoir Properties, and Well Performance in Shale Gas Reservoirs

2010 ◽  
Author(s):  
Mark A. Miller ◽  
Creties David Jenkins ◽  
Rakesh Roshan Rai
Keyword(s):  
Shale Gas ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Reservoir Properties ◽  
Fracture Characteristics ◽  
Shale Gas Reservoirs ◽  
Modeling Techniques ◽  
Production Modeling

From Streamlines to Fast Marching: Rapid Simulation and Performance Assessment of Shale-Gas Reservoirs by Use of Diffusive Time of Flight as a Spatial Coordinate

SPE Journal ◽  
2016 ◽  
Vol 21 (05) ◽  
pp. 1883-1898 ◽  
Author(s):  
Yanbin Zhang ◽  
Neha Bansal ◽  
Yusuke Fujita ◽  
Akhil Datta-Gupta ◽  
Michael J. King ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Performance Assessment ◽  
Shale Gas ◽  
History Matching ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Fast Marching ◽  
Shale Gas Reservoirs ◽  
And Performance ◽  
Diffusivity Equation

Summary Current industry practice for characterization and assessment of unconventional reservoirs mostly uses empirical decline-curve analysis or analytic rate- and pressure-transient analysis. High-resolution numerical simulation with local perpendicular bisector (PEBI) grids and global corner-point grids has also been used to examine complex nonplanar fracture geometry, interaction between hydraulic and natural fractures, and implications for the well performance. Although the analytic tools require many simplified assumptions, numerical-simulation techniques are computationally expensive and do not provide the more-geometric understanding derived from the depth-of-investigation (DOI) and drainage-volume calculations. We propose a novel approach for rapid field-scale performance assessment of shale-gas reservoirs. Our proposed approach is dependent on a high-frequency asymptotic solution of the diffusivity equation in heterogeneous reservoirs and serves as a bridge between simplified analytical tools and complex numerical simulation. The high-frequency solution leads to the Eikonal equation (Paris and Hurd 1969), which is solved for a “diffusive time of flight” (DTOF) that governs the propagation of the “pressure front” in the reservoir. The Eikonal equation can be solved by use of the fast-marching method (FMM) to determine the DTOF, which generalizes the concept of DOI to heterogeneous and fractured reservoirs. It provides an efficient means to calculate drainage volume, pressure depletion, and well performance and can be significantly faster than conventional numerical simulation. More importantly, in a manner analogous to streamline simulation, the DTOF can also be used as a spatial coordinate to reduce the 3D diffusivity equation to a 1D equation, leading to a comprehensive simulator for rapid performance prediction of shale-gas reservoirs. The speed and versatility of our proposed method makes it ideally suited for high-resolution reservoir characterization through integration of static and dynamic data. The major advantages of our proposed approach are its simplicity, intuitive appeal, and computational efficiency. We demonstrate the power and utility of our method by use of a field example that involves history matching, uncertainty analysis, and performance assessment of a shale-gas reservoir in east Texas. A sensitivity study is first performed to systematically identify the “heavy hitters” affecting the well performance. This is followed by history matching and an uncertainty analysis to identify the fracture parameters and the stimulated-reservoir volume. A comparison of model predictions with the actual well performance shows that our approach is able to reliably predict the pressure depletion and rate decline.

Modeling Well Performance in Shale-Gas Reservoirs

2009 ◽  
Author(s):  
C.L. Cipolla ◽  
E.P. Lolon ◽  
J.C. Erdle ◽  
V. Tathed
Keyword(s):  
Shale Gas ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs

Numerical Simulation of Shale Gas Production

2011 ◽  
Vol 402 ◽  
pp. 804-807 ◽  
Author(s):  
Song Ru Mu ◽  
Shi Cheng Zhang
Keyword(s):  
Shale Gas ◽  
Simulation Models ◽  
Fracture Network ◽  
Gas Production ◽  
Wire Mesh ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Shale Gas Reservoirs ◽  
Shale Reservoirs ◽  
Fracture Mapping

Shale gas reservoirs require a large fracture network to maximize well performance. Microseismic fracture mapping has shown that large fracture networks can be generated in many shale reservoirs. The application of microseismic fracture mapping measurements requires estimation of the structure of the complex hydraulic fracture or the volume of the reservoir that has been stimulated by the fracture treatment. There are three primary approaches used to incorporate microseismic measurements into reservoir simulation models: discrete modeling of the complex fracture network, wire-mesh model, and dual porosity model. This paper discuss the different simulation model, the results provided insights into effective stimulation designs and flow mechanism for shale gas reservoirs.

A New Probabilistic Approach for Uncertainty Quantification in Well Performance of Shale Gas Reservoirs

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 2038-2048 ◽  
Author(s):  
Wei Yu ◽  
Xiaosi Tan ◽  
Lihua Zuo ◽  
Jenn-Tai Liang ◽  
Hwa C. Liang ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Shale Gas ◽  
Probabilistic Approach ◽  
Model Parameters ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Production Forecasting ◽  
Shale Gas Reservoirs ◽  
Decline Curve ◽  
Curve Model

Summary Over the past decade, technological advancements in horizontal drilling and multistage fracturing enable natural gas to be economically produced from tight shale formations. However, because of limited availability of the production data as well as the complex gas-transport mechanisms and fracture geometries, there still exist great uncertainties in production forecasting and reserves estimation for shale gas reservoirs. The rapid pace of shale gas development makes it important to develop a new and efficient probabilistic-based methodology for history matching, production forecasting, reserves estimates, and uncertainty quantification that are critical for the decision-making processes. In this study, we present a new probabilistic approach with the Bayesian methodology combined with Markov-chain Monte Carlo (MCMC) sampling and a fractional decline-curve (FDC) model to improve the efficiency and reliability of the uncertainty quantification in well-performance forecasting for shale gas reservoirs. The FDC model not only can effectively capture the long-tail phenomenon of shale gas-production decline curves but also can obtain a narrower range of production prediction than the classical Arps model. To predict the posterior distributions of the decline-curve model parameters, we use a more-efficient adaptive Metropolis (AM) algorithm in place of the standard Metropolis-Hasting (MH) algorithm. The AM algorithm can form the Markov chain of decline-curve model parameters efficiently by incorporating the correlation between the model parameters. With the predicted posterior distributions of the FDC model parameters generated by the AM algorithm, the uncertainty in production forecasts and estimated-ultimate-recovery (EUR) prediction can then be quantified. This work provides an efficient and robust tool that is based on a new probabilistic approach for production forecasting, reserves estimations, and uncertainty quantification for shale gas reservoirs.

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