Artificial Intelligence (AI) technologies are being increasingly used in the Oil and Gas (O&G) industry to optimize production, reduce operational costs and maximize efficiency. According to a Markets and Markets report, AI in the global oil and gas market is expected to grow from an estimated USD 1.57 billion in 2017 to USD 2.85 billion by 2022, at a CAGR of 12.66%. The oil and gas enterprises are seeking novel approaches to address the issues that plague the industry at present. In view of the falling fuel prices, concerns over the environmental impact of energy production and personnel safety, companies are leveraging technological innovations such as AI to optimize processes and maximize the returns on investment. In this report, we present insights and trends related to the AI technologies used in the Oil and Gas industry, through a study of patents related to petroleum exploration and refining technology segments.

Schlumberger is a large, multinational corporation concerned primarily with the measurement, collection, and interpretation of data. For the past fifty years, most of the activities have been related to hydrocarbon exploration. The efficient location and production of hydrocarbons from an underground formation requires a great deal of knowledge about the formation, ranging in scale from the size and shape of the rock's pore spaces to the size and shape of the entire reservoir. Schlumberger provides its clients with two types of information: measurements, called logs, of the petrophysical properties of the rock around the borehole, such as its electrical, acoustical, and radioactive characteristics; and in terpretations of these logs in terms of geophysical properties such as porosity and mineral composition. Since log interpretation is expert skill, the emergence of expert systems technology prompted Schlumberger's initial interest in Artificial Intelligence.

Oilfield service giant Schlumberger has inked a deal to expand its offerings of artificial intelligence products and services. Oilfield service giant Schlumberger has inked a deal to expand its offerings of artificial intelligence products and services. Oilfield service giant Schlumberger has inked a deal to expand its offerings of artificial intelligence products and services. Oilfield service giant Schlumberger has inked a deal to expand its offerings of artificial intelligence products and services. Oilfield service giant Schlumberger has inked a deal to expand the use of artificial intelligence technology in the oil patch.

Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field.

Schlumberger, Chevron and Microsoft have launched a cloud-based artificial intelligence platform to improve a digital services in the oil field. The companies issued a joint statement from the SIS Global Forum in Monaco announcing the launch of DELFI, a cloud-based platform loaded with software for exploration, development, production, storage tanks and pipeline projects. "Never before has our industry seen a collaboration of this kind, and of this scale," Schlumberger CEO Olivier Le Peuch said in a statement. "Working together will accelerate faster innovation with better results, marking the beginning of a new era in our industry that will enable us to elevate performance across our industry's value chain." Service Sector: Millennials now make up majority of Schlumberger's workforce Initially developed by Schlumberger for Chevron, DELFI resides entirely on Microsoft's Azure cloud computing platform.

Enabling organizations to capture, share, and apply the collective experience and know-how of their people is seen as fundamental to competing in the knowledge economy. As a result, there has been a wave of enthusiasm and activity centered on knowledge management. To make progress in this area, issues of technology, process, people, and content must be addressed. In this article, we develop a road map for knowledge management. It begins with an assessment of the current state of the practice, using examples drawn from our experience at Schlumberger.

Design has long been an area of particular interest for AI researchers. Herbert Simon's 1968 Karl Taylor Compton lectures on the sciences of the artificial included substantial material on design. However, only recently have design researchers embraced paradigms from AI and AI researchers chosen design as a domain to study. Design research is a relatively new field, commencing in the 1960s with developments in design theories and methodologies. Although the results of the early design research produced domainindependent approaches to understanding and structuring design, the designers themselves were more comfortable with research that was specific to their own discipline.

In the lead article, Paul Cohen analyzes over 1.50 papers that were presented at the national conference last summer. Based on this analysis, he makes some interesting observations on the types of research in which we currently engage. Most research (or at least most research considered worthy of presentation by the AAAI-90 Program Committee) follows one of two strategies, according to Cohen's statistical analysis. One strategy is model oriented; that is, formal models of symbolic problem solving are hypothesized to be applicable to particular situations and then often tested on toy problems. The second strategy is system oriented; that is, it emphasizes the building of systems to solve difficult real-world problems.

AI activities are also being pursued at other Schlumberger locations, often jointly with SDR The locations related to logging and interpretation include: Schlumberger-Doll Research, Ridgefield, Connecticut (Contact: Peter Wu'l); Schlumberger Well Services, Austin, Texas (Contact: Scott Gut/my); Schlumberger Well Services, Houston, Texas (Contact: Scott Ma&s); Nippon Schlumberger, K K, Tokyo, Japan (Contact: Dennzs O'NezU); I&ude et Production Schlumbcraer. Other Schlumberger companies involied in Ai research include! Expert Systems Current work in expert, systems is concerned with developing techniques for building more robust and versatile log interpretation systems. One shortcoming of "first generation" expert systems, such as the Dipmeter Advisor, is their inability to reason about the task that they attempt to perform. Any description of the overall task is usually procedurally encoded and unavailable for examination.

During the past year we have been receiving reports of AAAIsponsored symposia and workshops faster than we can publish them. We also received two substantial bibliographies for which we did not have adequate space in any single issue. Peter Patel-Schneider and I agreed that these articles contained a great deal of useful and up-to-date information on the current state of the art in many different subdomains of AI, and should be distributed to all AAAI members. However, given the time value of these documents, we simply could not just put them in the queue for publication sometime in the dim future. Contained herein are reports from three of the 1990 Spring symposia that link AI with other disciplines: molecular biology, education, and minimallength encoding.