This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 144334, ’Automation of the Oilfield Asset Through an Artificial- Intelligence-Based Integrated-Production- Management Architecture,’ by Cesar Bravo, SPE, Halliburton, and Luigi Saputelli, SPE, Hess Corporation; Jose Aguilar Castro, Addison Rios, and Francklin Rivas, Universidad de los Andes; and Joseph Aguilar- Martin, CTAE - Aerospace Research and Technology Center, prepared for the 2011 SPE Digital Energy Conference and Exhibition, The Woodlands, Texas, 19-21 April. The paper has not been peer reviewed.

Integrated asset management is highly complex and requires the combined effort of several disciplines and technological tools. Orchestration of disciplines, workflow tools, and available data are important issues. Resource negotiation, communication language, and decision-making protocols are minor issues that exacerbate the problem, resulting in poor and delayed decision making. This study approached these challenges through implementation of distributed-artificial-intelligence-based architecture designed for automated production management.


As systems technology improves, oil-production-automation systems have become complex, and management of oil-production assets requires new methods of work and new technological tools that allow collaborative efforts between all those involved in the production process to support accurate and timely decision making. This collaboration has several names in literature. For this study, the phrase integrated production management (IPM) was used.

Many studies about IPM address enterprise-information-system integration as obtaining all information related to the production process to achieve the entire vision of the asset’s state. Several oil-production companies are developing projects oriented to IPM. IPM requires defining data models that enable information interchange between various applications of the enterprise. There are several ontological frameworks applied to the information interchange between production applications. This study proposed a complete approach over the technological tools required to support IPM and is based on the definition of an IPM architecture (IPMA).

Proposed IPMA

This architecture consists of the technological tools required for the acquisition, treatment, and analysis of the process information. The IPMA also considers business-process automation and decision-making support. The architecture has three layers, as shown in Fig. 1.

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