Synopsis "Artificial Intelligence For Petroleum Production"
Artificial intelligence is changing petroleum production—but turning promising algorithms into dependable field results requires far more than technical hype. Artificial Intelligence for Petroleum Production gives you a practical foundation for understanding how machine learning, automation, and digital transformation can support decisions throughout the upstream production lifecycle. It connects essential AI concepts with the physical realities, operational constraints, and high-consequence decisions encountered in oil and gas operations. Inside, you will learn how to: Prepare sensor, SCADA, historian, well-test, and reservoir data for reliable analysis Apply regression, classification, time-series forecasting, and deep learning to petroleum problems Improve drilling optimization, wellbore stability analysis, formation evaluation, and well placement Explore data-driven production forecasting, reservoir characterization, and history matching Use AI for artificial-lift optimization, flow assurance, predictive maintenance, and safety monitoring Understand digital twins, physics-informed models, model drift, governance, and production-grade deployment Recognize when engineering judgment should override an algorithmic recommendation Structured across 28 focused chapters, the book moves from fundamental machine learning concepts to deployment, governance, security, and workforce adoption. Rather than treating artificial intelligence as a replacement for petroleum expertise, it shows how AI can extend engineering judgment across larger datasets, faster response times, and complex operating environments. Whether you are a petroleum engineer, production specialist, data professional, technical manager, researcher, or student, this guide will help you understand where AI adds genuine value—and where its limitations must be respected. Build the practical foundation you need to navigate the digital future of petroleum production—get your copy today.
