Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
An integrated process management system is the key to success of an enterprise working in the exploration business. Classic project management consists of the creation of an expectation, the well plan, appropriate process monitoring and the post analysis of the project through the comparison of the expectation and the actual process. As a result of the post analysis lessons learned are compiled. The quality of this traditional project management is strongly dependent on information flow. State-of-the-art reporting on a drilling rig is recurrent human reporting such as the classic morning report, although there are a lot more sources of information available. The presented concept of automated reporting, away from human observations, is ensured by utilizing rig sensor data. The automated reporting provides management, engineering and operations with the level of detail and granularity of objective, high quality information they need. It also offers the possibility of knowledge management and exchange at the same organizational level providing different "views" of the same project. This means that operations receive information on detailed process parameters, engineering on essential design variables for future wells and management on cost. Along with traditional project management this new automated reporting enables continuous, real-time project tracking and analysis even with a declining workforce because all information is bundled and under "central control". Automated reporting is able to cover the life-cycle of every well construction and allows the establishment of high quality benchmarks for future planning and project evaluation in real-time. Moreover, it enhances the learning and experience level of every single person involved in the process. Finally, it offers the possibility to manage multiple concurrent projects such as a company's fleet of drilling rigs to be able to optimize resource allocation and project economics. Introduction The successful construction and operation of oil and gas wells depends to a high degree on the experience of the people involved and their evaluation and management of more or less unknown factors (e.g. geology). The aim of this paper is to outline a concept which provides a platform that allows the optimal use of the knowledge of an organization and to support all decisions and actions during the life-cycle of a well construction project as much as possible. Providing the possibility of a continuous and real-time comparison of plan and actual at the proper technical and commercial level of detail is the key for a successful project execution. All data collected for the purpose of analysis is completely integrated into a domain model. The result is the description of the "Drilling Process" over time. The key element is a high level of automation to increase efficiency and improve the quality of conventional morning reporting, e.g. using rig sensor data. The derivation of a highly accurate drilling operations plan forms the basis for further performance improvement. Existing plans are often a "self-fulfilling prophecy" in terms of performance. The current level of reporting fulfills its purpose as an administrative tool, but is not the ideal basis for the planning of future wells. Another point is the distribution of the right data at the proper level of detail to different organizational levels. The requirements vary from the driller, who essentially needs information about the use of the brake-handle of the drawworks, to upper management who want overall cost and time performance. In order to fulfill these requirements a new way of reporting, processing of data and analysis, together with visualization has to be developed. The challenge is the integration of subjective experience, the measured state of the system and the financial aspects of a project. A key element is to increase the usability of information with less data entry effort. Data quality management is also of central importance.
An integrated process management system is the key to success of an enterprise working in the exploration business. Classic project management consists of the creation of an expectation, the well plan, appropriate process monitoring and the post analysis of the project through the comparison of the expectation and the actual process. As a result of the post analysis lessons learned are compiled. The quality of this traditional project management is strongly dependent on information flow. State-of-the-art reporting on a drilling rig is recurrent human reporting such as the classic morning report, although there are a lot more sources of information available. The presented concept of automated reporting, away from human observations, is ensured by utilizing rig sensor data. The automated reporting provides management, engineering and operations with the level of detail and granularity of objective, high quality information they need. It also offers the possibility of knowledge management and exchange at the same organizational level providing different "views" of the same project. This means that operations receive information on detailed process parameters, engineering on essential design variables for future wells and management on cost. Along with traditional project management this new automated reporting enables continuous, real-time project tracking and analysis even with a declining workforce because all information is bundled and under "central control". Automated reporting is able to cover the life-cycle of every well construction and allows the establishment of high quality benchmarks for future planning and project evaluation in real-time. Moreover, it enhances the learning and experience level of every single person involved in the process. Finally, it offers the possibility to manage multiple concurrent projects such as a company's fleet of drilling rigs to be able to optimize resource allocation and project economics. Introduction The successful construction and operation of oil and gas wells depends to a high degree on the experience of the people involved and their evaluation and management of more or less unknown factors (e.g. geology). The aim of this paper is to outline a concept which provides a platform that allows the optimal use of the knowledge of an organization and to support all decisions and actions during the life-cycle of a well construction project as much as possible. Providing the possibility of a continuous and real-time comparison of plan and actual at the proper technical and commercial level of detail is the key for a successful project execution. All data collected for the purpose of analysis is completely integrated into a domain model. The result is the description of the "Drilling Process" over time. The key element is a high level of automation to increase efficiency and improve the quality of conventional morning reporting, e.g. using rig sensor data. The derivation of a highly accurate drilling operations plan forms the basis for further performance improvement. Existing plans are often a "self-fulfilling prophecy" in terms of performance. The current level of reporting fulfills its purpose as an administrative tool, but is not the ideal basis for the planning of future wells. Another point is the distribution of the right data at the proper level of detail to different organizational levels. The requirements vary from the driller, who essentially needs information about the use of the brake-handle of the drawworks, to upper management who want overall cost and time performance. In order to fulfill these requirements a new way of reporting, processing of data and analysis, together with visualization has to be developed. The challenge is the integration of subjective experience, the measured state of the system and the financial aspects of a project. A key element is to increase the usability of information with less data entry effort. Data quality management is also of central importance.
This paper describes the principles for preparing a convincing business case. The focus is on technology investments in production operations. There seems to be wide-spread support in most large organizations for the implementation of new technology. The challenge is in speaking to decision makers in a clear and convincing manner. A good business case describes clearly what the investment is, what the risks are, and what the expected benefits are. Introduction Getting acceptance of new computing, automation and communications technology (CACT) in the oil patch is always a challenge - one that begins and ends with a convincing business case. A good business case speaks to decision makers in a language they can understand. It outlines in clear terms the benefits of the investment, its costs and the risks involved. Each needs to be presented clearly, openly and honestly. This paper focuses on preparing a good business case for investments in CACT in oil and gas production. The applications in mind have to do with real-time operations, smart fields and fields of the future, areas of rapidly emerging technology. There seems to be broad support at the leadership levels in most large oil and gas companies for a version of the Digital Oilfield as a strategic direction, but difficulty at the implementation level in building a convincing case for investment. Implementation seems to be slow. Perhaps this is because generating convincing business cases for incremental investments is challenging. This paper is intended to provide practical how-to advice. Principles of a Good Business Case A good business case is based on five principles:Value is created only when good decisions are made and implemented. The value of an oil and gas asset derives from the decisions that were made and implemented in the past - to drill wells, to complete them, to implement recovery processes and to install facilities. Once the decisions and investments are made, the assets are operated in a fashion to capture the intended value. Oil fields don't exactly run themselves, of course, because numerous decisions are made every day to keep them running smoothly. Lift changes, new choke settings, changes in injection and production rates and set point adjustments all are designed to make sure that the value of the original investment is achieved. Added value is created when decisions are made and implemented to add new assets, modify existing ones, or to change operating practices or procedures. It is important that these decisions be the right decisions made at the right time. Whether they are, depends greatly on the quality of the information available at the time.CACT investments impact decisions. Computers don't really produce oil, at least not like wells, platforms and facilities do. What they do, is facilitate decisions, such as the ones described above to maintain or add assets. Thus the value of investments in CACT stuff derives from its ability to improve the decision making process or to improve the decisions that are made. By computing, automation and communications technology we include all aspects of their use. This includes data acquisition, storage, software applications, decision tools, automation, SCADA, and devices.
Recently, the industry has seen an enormous increase in the amount of upstream data delivered with fine resolution and accuracy as provided by downhole monitoring equipment. Downhole measurements include distributed temperatures along the wellbore, wellbore pressure and temperature at discrete points, zonal flow rates, equipment performance data such as ESP operating efficiency, and downhole and surface chemical injection data. Even though the potential benefits of these measurements are recognized, practical models and processes that take full advantage of the actual data have not been well established. Intelligent wells are gaining momentum in the oil and gas industry for production optimization, but utilization of this technology is limited to a single well or small group of wells, addressing somewhat localized optimization. Ultimate production optimization achieves higher reservoir recovery through incremental hydrocarbon production, and it needs a higher view than a well-centric approach. Considering that multiple intelligent wells comprise an intelligent field, all the data coming from each intelligent well should be brought together as input for a global optimization. This is analogous to bringing a fuzzy picture of a puzzle into focus as all the pieces fall into place. Similarly, to optimize a 5-spot waterflood, each well in the pattern should be intelligently controlled all the time and, in addition, they need to be optimized relative to each other so that the flood front movement is managed for maximum sweep efficiency. Optimization at this level is accomplished through an integrated use of reservoir/well modeling and real time data acquired through continuous downhole measurements. Measured data enables active model tuning, which in turn improves ongoing reservoir performance prediction. Sensitivity analyses find the optimal configuration of the intelligent wellbore components in injection and production wells that enables active waterflood front control. This paper discusses methodologies used for the control mechanism, including a simplified reservoir model, continuous monitoring data, and a multi-well optimization process. Visualization and control of water flood efficiency, continuous tuning of the reservoir/well model, improved performance prediction and full utilization of real time data are some of the benefits from the process developed. Introduction If one were to search for publications on Smart Fields, I-Fields, Digital Oil Fields, Fields of the Future and other catch words, surely many would cover the same ground as ours with a few exceptions (Vachon et al. 2006, Saputelli et al. 2003, Sarma et al. 2005, Purves et al. 1997, Hardy et al. 1982, Going et al. 2006, Oberwinkler et al. 2004, Brouwer et al. 2004, Oberwinkler et al. 2005, Silin et al. 2005). This paper describes some of the methodologies we used and workflows which are associated with modeling, monitoring, optimizing and subsequent control of the various surface and downhole controls available in this system. Our approach goes both one step deeper by showing the exact workflows' details and also one step back by avoiding unnecessary confusion with complex software design principles. We will first describe our system approach for this inverted 5-spot water flood as it pertains to overall objectives and how the hardware system, its constraints and monitoring thereof, were used to provide relevant data based on our assumptions. Next we will describe the modeling approach and how we are performing the system optimization. Following this we will discuss the workflows that were created to capture the movement of data and information that is used for active water flood management as it pertains to several types of scenarios that are likely to occur during the lifetime of the system. These include a high level generic production workflow followed by several specific examples such as active water management and how the system reacts to an unplanned shut-in.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.