Artificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E & P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.

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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.