Artificial intelligence (AI) has been used for more than two decades as a development tool for solutions in several areas of the E&P industry: virtual sensing, production control and optimization, forecasting, and simulation, among many others. Nevertheless, AI applications have not been consolidated as standard solutions in the industry, and most common applications of AI still are case studies and pilot projects. In this work, an analysis of a survey conducted on a broad group of professionals related to several E&P operations and service companies is presented. This survey captures the level of AI knowledge in the industry, the most common application areas, and the expectations of the users from AI-based solutions. It also includes a literature review of technical papers related to AI applications and trends in the market and R&D. The survey helped to verify that (a) data mining and neural networks are by far the most popular AI technologies used in the industry; (b) approximately 50% of respondents declared they were somehow engaged in applying workflow automation, automatic process control, rule-based case reasoning, data mining, proxy models, and virtual environments; (c) production is the area most impacted by the applications of AI technologies; (d) the perceived level of available literature and public knowledge of AI technologies is generally low; and (e) although availability of information is generally low, it is not perceived equally among different roles. This work aims to be a guide for personnel responsible for production and asset management on how AI-based applications can add more value and improve their decision making. The results of the survey offer a guideline on which tools to consider for each particular oil and gas challenge. It also illustrates how AI techniques will play an important role in future developments of IT solutions in the E&P industry.

Artificial intelligence (AI) has been used for more than 2 decades as a development tool for solutions in several areas of the exploration and production (E&P) industry: virtual sensing, production control and optimization, forecasting, and simulation, among many others. Nevertheless, AI applications have not been consolidated as standard solutions in the industry, and most common applications of AI still are case studies and pilot projects.In this work, an analysis of a survey conducted on a broad group of professionals related to several E&P operations and service companies is presented. This survey captures the level of AI knowledge in the industry, the most common application areas, and the expectations of the users from AI-based solutions. It also includes a literature review of technical papers related to AI applications and trends in the market and in research and development.The survey helped to verify that (a) data mining and neural networks are by far the most popular AI technologies used in the industry; (b) approximately 50% of respondents declared they were somehow engaged in applying workflow automation, automatic process control, rule-based case reasoning, data mining, proxy models, and virtual environments; (c) production is the area most affected by the applications of AI technologies; (d) the perceived level of available literature and public knowledge of AI technologies is generally low; and (e) although availability of information is generally low, it is not perceived equally among different roles.This work aims to be a guide for personnel responsible for production and asset management on how AI-based applications can add more value and improve their decision making. The results of the survey offer a guideline on which tools to consider for each particular oil and gas challenge. It also illustrates how AI techniques will play an important role in future developments of information-technology (IT) solutions in the E&P industry. IntroductionAlthough there is hardly a rigorous definition of the term "artificial intelligence" that is unequivocally accepted, the tools of AI and its intended uses have been well studied for decades and many applications have appeared. Loosely speaking, AI is the capability of machines (usually in the form of computer hardware and software) to mimic or exceed human intelligence in everyday engineering and scientific tasks associated with perceiving, reasoning, and acting. Because human intelligence is multifaceted, so is AI, comprising goals that range from knowledge representation and reasoning to learning, visual perception, and language understanding (Winston 1992). AI techniques have been present in the E&P industry for many years. A quick literature search reveals application of AI in SPE scientific and engineering papers as early as in the 1970s. There are numerous references about the

Intelligent digital oilfield (iDOF) operations have gained momentum in the past few years, transformed from being merely a vision to real-world projects with quantifiable value. Challenges such as increased energy demand and diminishing new discoveries, coupled with a lack of specialist-domain expertise and trained personnel to efficiently operate assets, have forced operators to rethink the traditional way of asset management to increase productivity and operational efficiency. The amount of information that asset managers now have to make decisions has increased dramatically in the last few years. More data about a problem can lead to improved decisions, but it also increases the complexity of the decision-making process. Asset teams need tools and technologies to help them quickly and efficiently analyze and understand all this data so they make better, faster decisions. To help asset teams meet these challenges, a new generation of petroleum workflow automation integrates real-time data with asset models, helping team members to collaborate so they can better analyze data and more fully understand asset problems. We're calling this new generation of automated, intelligent workflows "smart flows." This approach is cutting-edge, but also more complex. The complexity is addressed with the use of artificial intelligence technology, such as proxy models and neural networks, coupled with a visualization engine to provide an effective visual data mining tool. The objective of this new generation of petroleum workflow automation is to provide integrated solutions to asset opportunities and guide the operations with instructions based on smart analysis and integrated visualization. This paper provides an overview of a workflow automation environment that is being implemented for a major operator.

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In the last decade, upstream oil industry faced an exponential increase of the use of real-time data, which lead to numerous digital oilfield (DOF) implementations. These have demonstrated the value to drive operations efficiency, optimize production, and maximize hydrocarbon recovery with better, faster decisions while reducing health, environmental and safety risks.Since the appearance of computers and the internet, many enabling technologies entered the oil-patch. Over the years, various areas improved as a result of significant commercial, corporate and academic efforts. However, some specific concerns remain be the same as a decade ago: data, value proposition, work processes, people skills and other aspects of change management. This paper focuses on the best practices that have made DOF implementations successful and the hard lessons learned. Many DOF implementations failed to deliver the expected value because of poor practices and misconceptions. These are presented in four interrelated areas: people, automated workflows, processes and technologies.