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Combining specialized software with advanced machine learning and domain expertise—i.e., Data Science—is increasingly used for optimizing high-value assets. This technical presentation describes value creation in the upstream oil and gas sector through the use of data analytics for optimizing wellbore placement and evaluating completion scenarios. Multiple examples illustrate how running simulations can uniquely create insights to optimize capital allocation and deliver better wells with a system that accesses hidden signals from the full spectrum of upstream data (geophysical, geological, drilling, completion, location, production, and financial), enabling unique and highly valuable optimization in support of decision processes.
The presentation will introduce reservoir diagnostics technologies used by QRI to identify field development opportunities in mature assets: new well locations, workovers, production and surface facility optimization, drilling and completion performance improvements. Over the past few years, through the application of new data mining and machine learning technologies, blended with classical engineering methods, QRI was able to reduce the time needed to fully analyze a reservoir from months to days.
Driving reservoir management to automation has been requiring a blend of experience-based workflows, classical engineering and geosciences models and cutting edge data management, data mining and machine learning methods.
The presentation will introduce the general framework followed and dive in more details into a couple of applied examples including the detection of behind pipe opportunities and new drilling target as well as the automated analysis of thousands of daily drilling reports to extract insights on how to improve drilling efficiency.
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Luther Birdzell is an engineer, data scientist, and entrepreneur who is passionate about intellectual, machine, and kinesthetic learning. For over 20 years, Luther has been teaching computers how to represent high-cost digital and physical assets. His contributions include an AI-enabled deep cycle lithium battery simulation system; the strategic vision that lead to commercializing tools to replace expensive data center assets with low-cost software during development and testing, which Computer Associates acquired in 2011; and founding OAG Analytics to establish a new standard for core planning functions in upstream. Luther holds two electrical engineering degrees from Dartmouth College.
As VP of Technology at QRI, Mr. Matringe leads a technology development effort aiming at accelerating and eventually automating Reservoir Management analysis. Prior, he held various management and engineering functions at QRI. Mr. Matringe started his industrial career as a reservoir engineer at Chevron, working on field development projects in the Middle-East Africa. His experience also includes research in Reservoir Simulation performed in the R&D teams of Chevron, ExxonMobil and ConocoPhillips, Stanford, MIT, and UC Berkeley. Mr. Matringe holds a BSc and MSc in Mechanical Engineering from ENSEEIHT, France and a MSc and PhD in Petroleum Engineering from Stanford University.