Unconventional Hydrocarbon Resources: Prediction and Modeling Using Artificial Intelligence Approaches
暫譯: 非常規碳氫資源：使用人工智慧方法的預測與建模

Enables readers to save time and effort in exploring and exploiting shale gas and other unconventional fossil fuels by making use of advanced predictive tools.

Unconventional Hydrocarbon Resources highlights novel concepts and techniques for the geophysical exploration of shale and other tight hydrocarbon reservoirs, focusing on artificial intelligence approaches for modeling and predicting key reservoir properties such as pore pressure, water saturation, and wellbore stability. Numerous application examples and case studies present real-life data from different unconventional hydrocarbon fields such as the Barnett Shale (USA), the Williston Basin (USA), and the Ahnet and Berkine Basins (Algeria).

Unconventional Hydrocarbon Resources explores a wide range of reservoir properties, including modeling of the geomechanics of shale gas reservoirs, petrophysics analysis of shale and tight sand gas reservoirs, and prediction of hydraulic fracturing effects, fluid flow, and permeability.

Sample topics covered in Unconventional Hydrocarbon Resources include:

• Calculation of petrophysical parameter curves for non-conventional reservoir modeling and characterization
• Comparison of the Levenberg-Marquardt and conjugate gradient learning methods for total organic carbon prediction in the Barnett shale gas reservoir
• Use of pore effective compressibility for quantitative evaluation of low resistive pays and identifying sweet spots in shale reservoirs
• Pre-drill pore pressure estimation in shale gas reservoirs using seismic genetic inversion
• Using well-log data to classify lithofacies of a shale gas reservoir

Unconventional Hydrocarbon Resources is a valuable resource for researchers and professionals working on unconventional hydrocarbon exploration and in geoengineering projects.