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Geocognitive Technologies Improve Basin- and Petroleum-System Analyses

Eni and IBM developed a cognitive engine exploiting a deep-learning approach to scan documents searching for basin geology concepts, extracting information about petroleum system elements (e.g., formation name; geological age; and lithology of source rocks, reservoirs, and seals), and enabling basin geologists to perform automated queries to collect all information related to a basin of interest. The cognitive engine succeeded in identifying the correct formations, lithologies, and geological ages of the petroleum systems with an accuracy in the range of 75 to 90%.

Introduction

While commercial databases often provide summary information about basins that can be extracted easily with queries or even interactive tools, the explorationist needs to integrate such information with more up-to-date and in-depth descriptions of structural and sedimentary events occurring in the basin, descriptions that can be found only in unstructured documents. Key information about basins can be scattered across paragraphs, tables, and image captions of hundreds of technical articles, or can be embedded within pictures. Even when exploiting a traditional search engine with the name of the desired basin, the results can be unsatisfactory: first, not all the results might be relevant; second, many different variants of the basin name are often used within publications. In the optimistic hypothesis that the subset of relevant documents is found by the search engine, all key concepts related to a basin need to be understood by the geologist by careful examination of the paper text and images.

Moreover, even if the published information (structured and unstructured) on a basin is found, there are different opinions expressed by different authors, in addition to the uncertainty of the data itself (such as the age of a formation or the details of the geological evolution of the basin), so that multiple conceptual models of the basin can be drawn from the uncertain and scarce information available. Some of these conceptual models are more probable while others are less probable, but sometimes the latter happen to be economically more valuable. Therefore, recovery of all relevant information about a basin is crucial, but also important is the preservation of differing opinions about the data—what might be termed minority reports.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197610, “Application of Geocognitive Technologies to Basin- and Petroleum-System Analyses,” by Paolo Ruffo, Marco Piantanida, SPE, and Floriana Bergero, Eni, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11–14 November. The paper has not been peer reviewed.
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Geocognitive Technologies Improve Basin- and Petroleum-System Analyses

01 January 2021

Volume: 73 | Issue: 1

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