Application of Artificial Intelligence Techniques in Drilling System Design and Operations: A State of the Art Review and Future Research Pathways
- Opeyemi Bello (Institute of Petroleum Engineering, Clausthal University of Technology) | Catalin Teodoriu (Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma) | Tanveer Yaqoob (Institute of Petroleum Engineering, Clausthal University of Technology) | Joachim Oppelt (Institute of Petroleum Engineering, Clausthal University of Technology) | Javier Holzmann (Institute of Petroleum Engineering, Clausthal University of Technology) | Alisigwe Obiwanne (Institute of Petroleum Engineering, Clausthal University of Technology)
- Document ID
- Society of Petroleum Engineers
- SPE Nigeria Annual International Conference and Exhibition, 2-4 August, Lagos, Nigeria
- Publication Date
- Document Type
- Conference Paper
- 2016. Society of Petroleum Engineers
- 5.2.1 Phase Behavior and PVT Measurements, 5 Reservoir Desciption & Dynamics, 5.2 Fluid Characterization, 7 Management and Information, 3.2.7 Lifecycle Management and Planning, 3 Production and Well Operations, 2.2 Installation and Completion Operations, 1.5 drill Bits, 5.6 Formation Evaluation & Management, 3.2 Well Operations and Optimization, 7.6 Information Management and Systems, 7.6.4 Data Mining, 5.5 Reservoir Simulation, 5.6.1 Open hole/cased hole log analysis, 1.10 Drilling Equipment, 7.1 Asset and Portfolio Management, 5.1 Reservoir Characterisation, 2.2 Installation and Completion Operations, 1.6 Drilling Operations, 7.6.6 Artificial Intelligence, 7.1.5 Portfolio Analysis, Management and Optimization, 5.5.8 History Matching
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Artificial Intelligence (AI) has found extensive usage in simplifying complex decision-making procedures in practically every competitive market field, and oil and gas upstream industry is no exception to it. AI involves the use of sophisticated networking tools and algorithms in solving multifaceted problems in a way that imitates human intellect, with the aim of enabling computers and machines to execute tasks that could earlier be carried out only through demanding human brainstorming. Unlike other simpler computational automations, AI enables the designed tools to "learn" through repeated operation, thereby continuously refining the computing capabilities as more data is fed into the system.
Over the years, AI has led to significant designing and computation optimizations in the global Petroleum Exploration and Production (E&P) industry, and its applications have only continued to grow with the advent of modern drilling and production technologies. Tools such as Artificial Neural Networks (ANN), Generic Algorithms, Support Vector Machines and Fuzzy Logic have a historic connection with the E & P industry for more than 16 years now, with the first application dated in 1989 for development of an intelligent reservoir simulator interface, and for well-log interpretation and drill bit diagnosis through neural networks. Devices and softwares with basis from the above mentioned AI tools have been proposed to abridge the technology gaps hindering automated execution and monitoring of key reservoir simulation, drilling and completion procedures including seismic pattern recognition, reservoir characterisation and history matching, permeability and porosity prediction, PVT analysis, drill bits diagnosis, overtime well pressure-drop estimation, well production optimization, well performance projection, well / field portfolio management and quick, logical decision making in critical and expensive drilling operations.
The paper reviews and analyzes this successful integration of AI techniques as the missing piece of the puzzle in many reservoir, drilling and production aspects. It provides an update on the level of AI involvement in service operations and the application trends in the industry. A summary of various research papers and reports associated with AI usage in the upstream industry as well as its limitations has been presented.
|File Size||3 MB||Number of Pages||22|
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