In the shale oil & gas industry, operators are looking toward big data analytics to optimize operations and reduce cost. In this paper, we mainly focus on how to assist operators in understanding the subsurface formation, thereby helping them make optimal decisions. A large number of geology reports and well logs describing the sub-surface have been accumulated over years. Issuing geology reports is more time consuming and depends more on the expertise of engineers than acquiring the well logs. To assist in issuing geology reports, we propose an encoder-decoder-based model to automatically generate rock descriptions in human-readable format from multivariate well logs. Due to the different formats of data, this task differs dramatically from image and video captioning. The challenges are how to model structured rock descriptions and leverage the information in multivariate well logs. To achieve this, we design a hierarchical structure and two forms of attention for the decoder. Extensive validations are conducted on public well data of North Dakota in the United States. We show that our model is effective in generating rock descriptions. The two forms of attention enable the provision of a better insight into relations between well-log types and rock properties with our model from a data-driven perspective.

In the shale oil & gas industry, operators are looking toward big data and new analytics tools and techniques to optimize operations and reduce cost. Formation evaluation is one of the most crucial steps before the fracturing operation. To assist engineers in understanding the subsurface and in turn make optimal operations, we focus on learning semantic relations between geology reports and well logs, which are collected during down-hole drilling. The challenges are how to represent the features of the geology reports and the well logs collected at measured depths and how to effectively embed them into a common feature space. We propose both linear and nonlinear (artificial neural network) models to achieve such an embedding. Extensive validations are conducted on public well data of North Dakota in the United States. We empirically discover that both geology reports and well logs follow a neighborhood property measured by geological distance. We show that this spatial information is highly effective in both the linear and nonlinear models and our nonlinear model with the spatial information performs the best among the state-of-the-art methods.

The importance of patents is increasing in global society. In preparing a patent application, it is essential to search for related patents that may invalidate the invention. However, it is time-consuming to identify them among the millions of patents. This article proposes a patent-retrieval method that considers a claim structure for a more accurate search for invalidity. This method uses a claim text as input; it consists of two retrieval stages. In stage 1, general text analysis and retrieval methods are applied to improve recall. In stage 2, the top N documents retrieved in stage 1 are rearranged to improve precision by applying text analysis and retrieval methods using the claim structure. Our two-stage retrieval introduces five precision-oriented analysis and retrieval methods: query-term extraction from a portion of a claim text that describes the characteristics of a claim; query term-weighting without term frequency; query term-weighting with “measurement terms”; text retrieval using only claims as a target; and calculating the relevant score by “partially” adding scores in stage 2 to those in stage 1. Evaluation results using test sets of the NTCIR4 Patent Retrieval Task show that our methods are effective, though the degree of the effectiveness varies depending on the test sets.

This paper describes the Patent Retrieval Task in the Fourth NTCIR Workshop, and the test collections produced in this task. We perform the invalidity search task, in which each participant group searches a patent collection for the patents that can invalidate the demand in an existing claim. We also perform the automatic patent map generation task, in which the patents associated with a specific topic are organized in a multi-dimensional matrix.

Reflecting the rapid growth in the utilization of large test collections for information retrieval since the 1990s, extensive comparative experiments have been performed to explore the effectiveness of various retrieval models. However, most collections were intended for retrieving newspaper articles and technical abstracts. In this paper, we describe the process of producing a test collection for patent retrieval, the NTCIR-3 Patent Retrieval Collection, which includes two years of Japanese patent applications and 31 topics produced by professional patent searchers. We also report experimental results obtained by using this collection to re-examine the effectiveness of existing retrieval models in the context of patent retrieval. The relative superiority among existing retrieval models did not significantly differ depending on the document genre, that is, patents and newspaper articles. Issues related to patent retrieval are also discussed.

The use of incremental relevance feedback and document clustering were investigated in an relevance feedback environment in which the number of relevance judgements was quite small. Through experiments on the TREC collection, the incremental relevance feedback approach was found not to improve the overall search effectiveness. The clustering approach was found to be promising, although it sometimes over-focuses on a particular topic in a query and ignores the others. To overcome this problem, a query-biased clustering algorithm was developed and shown to be effective.

Article Cluster-based text categorization: a comparison of category search strategies Share on Authors: Makoto Iwayama Advanced Research Laboratory, Hitachi Ltd., Hatoyama, Saitama 350-03, Japan Advanced Research Laboratory, Hitachi Ltd., Hatoyama, Saitama 350-03, JapanView Profile , Takenobu Tokunaga Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152, Japan Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152, JapanView Profile Authors Info & Claims SIGIR '95: Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrievalJuly 1995 Pages 273–280https://doi.org/10.1145/215206.215371Online:01 July 1995Publication History 63citation1,299DownloadsMetricsTotal Citations63Total Downloads1,299Last 12 Months38Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access