Application of data-driven analytics and predictive modeling in the oil and gas industry is fairly new. A handful of domain experts have dedicated extensive amount of time and effort to develop and present the next generation of tools that incorporates these technologies in the petroleum industry. Unfortunately, hypes, buzz words, and marketing schemes around data analytics have overwhelmed the petroleum industry in the past couple of years. Many with little to no understanding and knowledge of the physics and the geology of fluid flow through porous media have been marketing these hypes.
This course will demonstrate the power of Artificial Intelligence and Machine learning and the difference they can make for informed decision making when it comes to objectives such as infill location optimization and reservoir production and recovery optimization once domain expertise becomes the foundation of their use and application in the hydrocarbon reservoirs.
Data-Driven Reservoir Modeling (Reservoir Analytics) is defined as the application of Artificial Intelligence and Machine Learning in fluid flow through porous media. Data-Driven Reservoir Modeling (Reservoir Analytics) is the manifestation of the digital transformation as it applies to the subsurface modeling in the upstream exploration and production industry. Effective and impactful use of this technology, which is the future of reservoir simulation and modeling, is becoming the important point of competitive differentiation in our industry.
The foundation of Data-Driven Reservoir Modeling (Reservoir Analytics) is solid domain expertise (reservoir engineering, reservoir modeling, and reservoir management) and comprehensive understanding of physics and geology of fluid flow through porous media. Data-Driven Reservoir Modeling (Reservoir Analytics) overcomes the over-simplifications associated with applied statistics and curve fitting approaches (including CRM). The major distinguishing factors of Data Driven Reservoir Modeling (Reservoir Analytics) when compared to traditional numerical reservoir simulation are (a) avoidance of preconceived notions and biases, (b) lack of inclusion of significant approximations and simplifications, (c) complete automation of the history matching process, (d) generation of accurate and fast subsurface models for practical reservoir management, and (e) performing comprehensive and practical Field Development Planning (FDP), Production and Recovery Optimization (PRO), and Uncertainty Quantification (UQ) with tens of millions of simulation runs.
Data-Driven Reservoir Modeling (Reservoir Analytics) includes a set of tools and techniques that provides the means for extraction of patterns and trends from all field measured data (drilling, completion, formation, seismic, operation, production, well test, well logs, cores, etc.) and construction of predictive models that are validated through blind history matching in time and space. Data-Driven Reservoir Modeling (Reservoir Analytics) provides the ultimate assistance in short, medium, and long-term decision making and optimization.
Attendees will become familiar with the fundamentals of data-driven analytics, Artificial Intelligence and Machine learning including the most popular techniques used to apply them such as artificial neural networks, evolutionary computing, and fuzzy set theory.
This course will demonstrate through actual case studies (and real field data from thousands of wells) how to impact infill well placement, completion, and operational decision-making based on field measurements rather than human biases and preconceived notions.