{"title":"Building an Internet World Wide Web Server","authors":"A. A. Barnett, J. E. Russell","doi":"10.2118/30188-PA","DOIUrl":"https://doi.org/10.2118/30188-PA","url":null,"abstract":"","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126215031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Building Extensible Interactive Applications - A Case Study","authors":"R. Frost, Michael Diserens, G. Williams","doi":"10.2118/30191-PA","DOIUrl":"https://doi.org/10.2118/30191-PA","url":null,"abstract":"","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127857549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
More effective use of computing tools for production surveillance by operations and engineering personnel is critical to increases in profitability. Exxon`s Artificial Lift Workstation (ALWORKS) is a software package that allows easy access to electronically stored data and provides that data to various surveillance, analysis, and design applications with minimal user effort. Operations personnel have field-tested the software, and they have noted significant increases in productivity that will lead to increases in profitability.
{"title":"ALWORKS - An artificial lift surveillance tool","authors":"G. Burleson, J. Redden","doi":"10.2118/35218-PA","DOIUrl":"https://doi.org/10.2118/35218-PA","url":null,"abstract":"More effective use of computing tools for production surveillance by operations and engineering personnel is critical to increases in profitability. Exxon`s Artificial Lift Workstation (ALWORKS) is a software package that allows easy access to electronically stored data and provides that data to various surveillance, analysis, and design applications with minimal user effort. Operations personnel have field-tested the software, and they have noted significant increases in productivity that will lead to increases in profitability.","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125249286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As computing technology has evolved, so have the demands placed by the user community on the functions it provides. The expectations of software used in drilling operations are changing from a convenient way of producing the daily drilling report, to acquiring drilling data for use in engineering analysis programs. This data is expected to provide insight into an increasing array of complicated engineering operations questions. There are a number of commercial morning reporting systems in use throughout the industry. However, after collecting many years worth of drilling data, the engineer is finding that their morning reporting system cannot easily provide the answers to some very basic questions. Many providers of drilling information systems have built their applications founded on daily wellsite operations, using the latest Graphical User Interfaces (GUIs) to ensure their case of use. The data model is usually an afterthought, and is generally time rather than activity based. In some cases, the data model is used as a tool to further the system`s {open_quotes}user-friendliness{close_quotes}. This robs the application of its versatility in performing even some of the most fundamental analysis of the data. To date, neither POSC (Petrotechnical Open Systems Corporation) nor the PPDM (Public Petroleum Data Model)more » have initiated studies to develop a drilling data model. The authors will present a data model that was used as a basis for Shell Canada`s (SCan) Drilling Information System. The provision of a sound data model, that accurately reflects the fundamental data used by drilling (or other operations), is paramount to the success of any information system. Without it, the utilization of engineering operations analysis tools for optimization would be extremely difficult.« less
{"title":"Data model for drilling information and its application","authors":"C. Pratt, M. Barazzutti","doi":"10.2118/30181-PA","DOIUrl":"https://doi.org/10.2118/30181-PA","url":null,"abstract":"As computing technology has evolved, so have the demands placed by the user community on the functions it provides. The expectations of software used in drilling operations are changing from a convenient way of producing the daily drilling report, to acquiring drilling data for use in engineering analysis programs. This data is expected to provide insight into an increasing array of complicated engineering operations questions. There are a number of commercial morning reporting systems in use throughout the industry. However, after collecting many years worth of drilling data, the engineer is finding that their morning reporting system cannot easily provide the answers to some very basic questions. Many providers of drilling information systems have built their applications founded on daily wellsite operations, using the latest Graphical User Interfaces (GUIs) to ensure their case of use. The data model is usually an afterthought, and is generally time rather than activity based. In some cases, the data model is used as a tool to further the system`s {open_quotes}user-friendliness{close_quotes}. This robs the application of its versatility in performing even some of the most fundamental analysis of the data. To date, neither POSC (Petrotechnical Open Systems Corporation) nor the PPDM (Public Petroleum Data Model)more » have initiated studies to develop a drilling data model. The authors will present a data model that was used as a basis for Shell Canada`s (SCan) Drilling Information System. The provision of a sound data model, that accurately reflects the fundamental data used by drilling (or other operations), is paramount to the success of any information system. Without it, the utilization of engineering operations analysis tools for optimization would be extremely difficult.« less","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129232778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visualization of Reservoir Simulation Data With an Immersive Virtual Reality System","authors":"B. K. Williams","doi":"10.2118/27546-PA","DOIUrl":"https://doi.org/10.2118/27546-PA","url":null,"abstract":"","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122967273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Gas and Oil Technology Exchange and Communication Highway, (GO-TECH), provides an electronic information system for the petroleum community for the purpose of exchanging ideas, data, and technology. The personal computer-based system fosters communication and discussion by linking oil and gas producers with resource centers, government agencies, consulting firms, service companies, national laboratories, academic research groups, and universities throughout the world. The oil and gas producers are provided access to the GO-TECH World Wide Web home page via modem links, as well as Internet. The future GO-TECH applications will include the establishment of{open_quote}Virtual corporations {close_quotes} consisting of consortiums of small companies, consultants, and service companies linked by electronic information systems. These virtual corporations will have the resources and expertise previously found only in major corporations.
{"title":"Implementation of a World Wide Web Server for the Oil and Gas Industry","authors":"R. Blaylock, F. Martin, R. Emery","doi":"10.2118/30214-PA","DOIUrl":"https://doi.org/10.2118/30214-PA","url":null,"abstract":"The Gas and Oil Technology Exchange and Communication Highway, (GO-TECH), provides an electronic information system for the petroleum community for the purpose of exchanging ideas, data, and technology. The personal computer-based system fosters communication and discussion by linking oil and gas producers with resource centers, government agencies, consulting firms, service companies, national laboratories, academic research groups, and universities throughout the world. The oil and gas producers are provided access to the GO-TECH World Wide Web home page via modem links, as well as Internet. The future GO-TECH applications will include the establishment of{open_quote}Virtual corporations {close_quotes} consisting of consortiums of small companies, consultants, and service companies linked by electronic information systems. These virtual corporations will have the resources and expertise previously found only in major corporations.","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130626849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Well-test data have been used traditionally for determining a variety of reservoir parameters, such as average permeability, storage capacity, reservoir damage, presence of faults and fractures, and reservoir mechanism. A number of techniques, both conventional methods, such as type-curve matching and numerical simulation, and artificial intelligence (AI) methods, have been used for identifying well-test models. These methods are laborious and time-consuming and at times give incorrect results. Artificial neural networks (ANN`s) are recent developments in computer vision and image analysis. These networks are specialized computer software that generate a strategy to produce nonlinear mapping functions for complex problems. ANN`s are commonly used as a tool for recognizing an object or predicting an event given an associated pattern. Only a limited number of applications of ANN for analyzing well-test data have been reported. These applications are mostly model-specific (developed for specific reservoir models) and, hence, are not general enough. This paper presents a new method based on ANN`s that uses Kohonen`s self-organizing feature (SOF) mapping technique to identify well-test interpretation models. By grouping well-test data into distinct categories, the SOF algorithm produces a general mapping function. This method can help analyze well-test data from a large variety of reservoirs (including reservoirsmore » with faults, fractures, boundaries, etc.) more efficiently and inexpensively than was feasible previously.« less
{"title":"Well-Test Model Identification With Self-Organizing Feature Map","authors":"S. Sinha, M. Panda","doi":"10.2118/30216-PA","DOIUrl":"https://doi.org/10.2118/30216-PA","url":null,"abstract":"Well-test data have been used traditionally for determining a variety of reservoir parameters, such as average permeability, storage capacity, reservoir damage, presence of faults and fractures, and reservoir mechanism. A number of techniques, both conventional methods, such as type-curve matching and numerical simulation, and artificial intelligence (AI) methods, have been used for identifying well-test models. These methods are laborious and time-consuming and at times give incorrect results. Artificial neural networks (ANN`s) are recent developments in computer vision and image analysis. These networks are specialized computer software that generate a strategy to produce nonlinear mapping functions for complex problems. ANN`s are commonly used as a tool for recognizing an object or predicting an event given an associated pattern. Only a limited number of applications of ANN for analyzing well-test data have been reported. These applications are mostly model-specific (developed for specific reservoir models) and, hence, are not general enough. This paper presents a new method based on ANN`s that uses Kohonen`s self-organizing feature (SOF) mapping technique to identify well-test interpretation models. By grouping well-test data into distinct categories, the SOF algorithm produces a general mapping function. This method can help analyze well-test data from a large variety of reservoirs (including reservoirsmore » with faults, fractures, boundaries, etc.) more efficiently and inexpensively than was feasible previously.« less","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"9 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113974694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Minimizing investment in oil field development is an important subject that has attracted a considerable amount of industry attention. One method to reduce investment involves the optimal placement and selection of production facilities. Because of the large amount of capital employed in this process, saving a small percent of the total investment may represent a large monetary value. Algorithms using mathematical programming techniques that were designed to solve the proposed problem in a global optimal manner have been reported in the literature. Due to the high computational complexity and the lack of user friendly interfaces for data entry, model development and results display, decision makers are not willing to accept mathematical programming techniques. This paper describes an interactive, graphical software system that provides a global optimal solution to the problem of placement and selection of production facilities in oil field development processes. This software system can be used as an investment minimization tool and a scenario study simulator. The developed software system consists of five basic modules: An interactive data input unit, a cost function generator, an optimization unit, a graphic output display and a sensitivity analysis unit. The interactive data input unit allows users to enter cost, facility, andmore » constraints data; the cost function generator determines which cost function to use based on data input; the optimization unit allows users to select one of the two optimization algorithms: 0-1 integer programming with preprocessing and Lagrangian Relaxation; the graphic output unit displays the optimal solution graphically; and the sensitivity analysis unit allows users to perform if-then type analysis. Data from both on-shore and off-shore oil fields have been used to test the performance of the developed system. Significant reduction of computer run time and memory storage were obtained.« less
{"title":"A software system for oilfield facility investment minimization","authors":"Z. Ding, R. Startzman","doi":"10.2118/28252-PA","DOIUrl":"https://doi.org/10.2118/28252-PA","url":null,"abstract":"Minimizing investment in oil field development is an important subject that has attracted a considerable amount of industry attention. One method to reduce investment involves the optimal placement and selection of production facilities. Because of the large amount of capital employed in this process, saving a small percent of the total investment may represent a large monetary value. Algorithms using mathematical programming techniques that were designed to solve the proposed problem in a global optimal manner have been reported in the literature. Due to the high computational complexity and the lack of user friendly interfaces for data entry, model development and results display, decision makers are not willing to accept mathematical programming techniques. This paper describes an interactive, graphical software system that provides a global optimal solution to the problem of placement and selection of production facilities in oil field development processes. This software system can be used as an investment minimization tool and a scenario study simulator. The developed software system consists of five basic modules: An interactive data input unit, a cost function generator, an optimization unit, a graphic output display and a sensitivity analysis unit. The interactive data input unit allows users to enter cost, facility, andmore » constraints data; the cost function generator determines which cost function to use based on data input; the optimization unit allows users to select one of the two optimization algorithms: 0-1 integer programming with preprocessing and Lagrangian Relaxation; the graphic output unit displays the optimal solution graphically; and the sensitivity analysis unit allows users to perform if-then type analysis. Data from both on-shore and off-shore oil fields have been used to test the performance of the developed system. Significant reduction of computer run time and memory storage were obtained.« less","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125359108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The neural network technique that is a field of artificial intelligence (AI) has proved to be a good model classifier in all areas of engineering and especially, it has gained a considerable acceptance in well test interpretation model (WTIM) identification of petroleum engineering. Conventionally, identification of the WTIM has been approached by graphical analysis method that requires an experienced expert. Recently, neural network technique equipped with back propagation (BP) learning algorithm was presented and it differs from the AI technique such as symbolic approach that must be accompanied with the data preparation procedures such as smoothing, segmenting, and symbolic transformation. In this paper, we developed BP neural network with Hough transform (HT) technique to overcome data selection problem and to use single neural network rather sequential nets. The Hough transform method was proved to be a powerful tool for the shape detection in image processing and computer vision technologies. Along these lines, a number of exercises were conducted with the actual well test data in two steps. First, the newly developed AI model, namely, ANNIS (Artificial intelligence Neural Network Identification System) was utilized to identify WTIM. Secondly, we obtained reservoir characteristics with the well test model equipped with modified Levenberg-Marquartmore » method. The results show that ANNIS was proved to be quite reliable model for the data having noisy, missing, and extraneous points. They also demonstrate that reservoir parameters were successfully estimated.« less
{"title":"Development of the HT-BP Neural Network System for the Identification of a Well-Test Interpretation Model","authors":"W. Sung, I. Yoo, S. Ra, H. Park","doi":"10.2118/30974-PA","DOIUrl":"https://doi.org/10.2118/30974-PA","url":null,"abstract":"The neural network technique that is a field of artificial intelligence (AI) has proved to be a good model classifier in all areas of engineering and especially, it has gained a considerable acceptance in well test interpretation model (WTIM) identification of petroleum engineering. Conventionally, identification of the WTIM has been approached by graphical analysis method that requires an experienced expert. Recently, neural network technique equipped with back propagation (BP) learning algorithm was presented and it differs from the AI technique such as symbolic approach that must be accompanied with the data preparation procedures such as smoothing, segmenting, and symbolic transformation. In this paper, we developed BP neural network with Hough transform (HT) technique to overcome data selection problem and to use single neural network rather sequential nets. The Hough transform method was proved to be a powerful tool for the shape detection in image processing and computer vision technologies. Along these lines, a number of exercises were conducted with the actual well test data in two steps. First, the newly developed AI model, namely, ANNIS (Artificial intelligence Neural Network Identification System) was utilized to identify WTIM. Secondly, we obtained reservoir characteristics with the well test model equipped with modified Levenberg-Marquartmore » method. The results show that ANNIS was proved to be quite reliable model for the data having noisy, missing, and extraneous points. They also demonstrate that reservoir parameters were successfully estimated.« less","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125948539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many drilling information systems have come and gone over the past decade. Some systems were very sophisticated because they used the latest technologies and human interface. Many factors about these systems and their implementation, however, proved to be weak points. In some cases, the system may have been too expensive to develop and/or maintain, too difficult to use, or simply did not meet the client`s business need. The Mobil Drilling Data Center (DDC) is a successful drilling information management system that has been in continuous operation for 13 years. This paper outlines key factors that have made the DDC one of the industry`s most comprehensive and long-lived drilling information systems.
{"title":"A formula for the successful management of Drilling Information","authors":"M. Magner, J. Booth, J. W. Williams","doi":"10.2118/28223-PA","DOIUrl":"https://doi.org/10.2118/28223-PA","url":null,"abstract":"Many drilling information systems have come and gone over the past decade. Some systems were very sophisticated because they used the latest technologies and human interface. Many factors about these systems and their implementation, however, proved to be weak points. In some cases, the system may have been too expensive to develop and/or maintain, too difficult to use, or simply did not meet the client`s business need. The Mobil Drilling Data Center (DDC) is a successful drilling information management system that has been in continuous operation for 13 years. This paper outlines key factors that have made the DDC one of the industry`s most comprehensive and long-lived drilling information systems.","PeriodicalId":115136,"journal":{"name":"Spe Computer Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114449579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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