Shale Gas and Tight Oil Reservoir Simulation delivers the latest research and applications used to better manage and interpret simulating production from shale gas and tight oil reservoirs. Starting with basic fundamentals, the book then includes real field data that will not only generate reliable reserve estimation, but also predict the effective range of reservoir and fracture properties through multiple history matching solutions. Also included are new insights into the numerical modelling of CO2 injection for enhanced oil recovery in tight oil reservoirs. This information is critical for a better understanding of the impacts of key reservoir properties and complex fractures. - Models the well performance of shale gas and tight oil reservoirs with complex fracture geometries - Teaches how to perform sensitivity studies, history matching, production forecasts, and economic optimization for shale-gas and tight-oil reservoirs - Helps readers investigate data mining techniques, including the introduction of nonparametric smoothing models

The massive increase in energy demand and the related rapid development of unconventional reservoirs has opened up exciting new energy supply opportunities along with new, seemingly intractable engineering and research challenges. The energy industry has primarily depended on a heuristic approach—rather than a systematic approach—to optimize and tackle the various challenges when developing new and improving the performance of existing unconventional reservoirs. Industry needs accurate estimations of well production performance and of the cumulative estimated ultimate reserves, accounting for uncertainty. This Special Issue presents 10 original and high-quality research articles related to the modeling of unconventional reservoirs, which showcase advanced methods for fractured reservoir simulation, and improved production forecasting techniques.

Sets forth a rationalisation of stimulation using reservoir engineering concepts, and addresses topics such as formation characterisation, hydraulic fracturing and matrix acidizing. Formation damage, which refers to a loss in reservoir productivity, is also examined comprehensively.

This book examines the physical and economic characteristics of the global oil resource to explain why peak oil has been so poorly understood. The author draws on information held in oil industry datasets that are not widely available outside of the specialist literature, and describes a number of methods that have been successfully used to predict oil peaks. In contrast to the widely-held view that ‘all oil forecasts are wrong’, these methods correctly predicted the current peak in global conventional oil production. Current oil forecasts are then compared to evaluate the expected dates for regional and global oil peaks for conventional oil, all-oils, and all-liquids. The dates of global peaks in the production of all-oil and all-liquids appear to be reasonably soon, while the oil price that is needed to support these global production levels continues to rise. The world faces serious constraints in its oil supply, which accounts for about one-third of total world energy use, and over 90% of the fuel used for transportation. Readers of this book will gain a thorough understanding of the critical, but poorly understood, phenomenon of peak oil that has already had significant impacts on society in terms of high oil prices, and which will place increasing constraints on mankind’s supply of energy and economic well-being in the coming years.

Deep learning neural networks have become easy to define and fit, but are still hard to configure. Discover exactly how to improve the performance of deep learning neural network models on your predictive modeling projects. With clear explanations, standard Python libraries, and step-by-step tutorial lessons, you’ll discover how to better train your models, reduce overfitting, and make more accurate predictions.