The SPE Real Time Optimization Technical Interest Group conducted a survey of its members earlier this year to learn more about the barriers to implementation of this technology. Understanding the barriers better will allow us to focus on the more important issues. The survey was in two parts. The first asked about the overall process of getting from data to decisions and action. The second focused on the usage of technical and business tools. This paper summarizes the results of the survey. We found that each step in the process has considerable major and minor barriers to overcome. The most difficult steps were in the areas of business analysis, recommendations / decisions and taking action. These are areas where management can have the greatest impact through improved work processes, governance and procedures. The results also show there is considerable need for improved commercial technical and business analysis tools Introduction The digital oilfield is the subject of a great deal of interest and emphasis these days. It has enormous potential, but seems to be slow in developing. This paper was conceived by the SPE Real Time Optimization Technical Interest Group (RTO TIG). (See http://www.merricksys.com/rto/) This is the fourth in a series of SPE papers sponsored by the RTO TIG1,2,3. In conducting this survey we were interested in learning more about the barriers to implementation of real time optimization technology, especially the barriers associated with getting from data to decisions and action. With the help of SPE International a survey of TIG membership was conducted early this year to gain insight into these barriers. Understanding them better will allow us to focus on the more important issues. A related analysis was published earlier by Liddell, Deaton and Mijares4. A summary of the results of this survey are contained in this paper. The Survey The survey was sent via e-mail to members of the RTO Technical Interest Group. The survey questionnaire was in two parts.The first asked about the overall process of effective implementation. Effective utilization of RTO involves a process of gathering and analyzing data, making recommendations and decisions and taking action (Figure 1). Perhaps the process was working smoothly everywhere, although we expected there are some snags somewhere.The survey recipients were asked to provide their perspective on where along the process the current difficulties lay. In other words, where is the wasted effort and lost time?The second part focused on the usage of technical and business analysis tools, where we suspected major barriers were limiting adoption. We were particularly interested in the application of analysis tools for technical and business applications. This questionnaire was focused primarily on wells and subsurface application tools. Respondents Over 200 people responded. About half worked for producing companies while the other half worked for providers - a fourth worked for service companies and a fourth were vendors, consultants and academics. About two-thirds worked onshore and one-third offshore. While similar, there were some differences between responses from the producers and the providers. In this paper we have highlighted results from the producers, but noted responses from providers as well.
For the past several years, the problem of reducing time-to-decision in field operations and capital projects has been repeatedly described and analyzed in qualitative and anecdotal terms. In this paper, we take an engineering approach to measure and understand the problem in quantitative and fact-based terms. We first review the mission of the SPE IT Technical Section-Oilfield Integration (SPE ITTS OI) subcommittee. Several contexts of oilfield integration and their role in Digital Oilfield of the Future (DOFF) initiatives are identified. We discuss the results of our study, and compare the results with those from other studies conducted by the SPE and also by two integrated oil companies (IOCs). We address the goal of "reducing time to decision," and show how even the most basic data-integration gaps can slow decisions with great economic impact. In information management and decision-making, the mondegreen "data commute" is the biggest problem area. The data commute absorbs over half the time that engineering and operations personnel could be spending making crucial decisions to effectively manage E&P assets. We review some case studies and their economic impact. Beyond "saving half of an engineer's time," several other more subtle gaps in the industry's current modeling and integration approaches are identified and evaluated. We conclude with showing how some of these gaps have been filled in other industries outside of oil & gas, and cite some examples that could be applicable in production surveillance and optimization. Introduction Every day engineers and geoscientists have to deal with time-consuming and manual search processes involving multiple applications, to answer even the most basic operational questions on the performance of individual wells to an entire field. DOFF initiatives are aimed at solving time-based problems by focusing on remote performance management and optimization, total producing asset awareness and visibility, right-time analysis and decision-making, and accurate and rapid operational execution. The oil & gas industry has repeatedly confirmed its shared interest and principles of DOFF, which first and foremost depends on enabling people to make decisions that avert problems, improve operations, and optimize production. The mission of the SPE ITTS OI subcommittee is to facilitate implementation of the digital oilfield through integration of information, technology, people, and processes. SPE ITTS OI identifies opportunities for improvement and supports the development and implementation of IT integration solutions, standards, and best practices spanning E&P business, surface, and subsurface domains. The SPE ITTS Oilfield Integration subcommittee and SPE Real-Time Optimization TIG have conducted several in-depth statistical studies that show that an unacceptable percentage of professionals in the E&P workforce are spending too much time searching for data, integrating it from multiple sources, and preparing it for analysis in applications.2,3 Once the data is in the application package for analysis, any updated or more recent data require a repeat of a tedious, uncreative, and nonproductive process to prepare and reload data. The time that could have otherwise been spent making operational or project decisions is lost completely to activity that has nothing to do with engineering and geoscience training. In this work, we recognize a fundamental tenet: Decisions drive actions that create value. We focus on the productive and economic impacts of making informed decisions sooner: "If I can make that decision accurately, but a month earlier, and can do this across many projects, many wells, many fields…". Obviously, faster decisions with precision have tremendous value, and provide much leverage in an industry hindered by a shortage of qualified people.
TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractOver the past ten years Chevron and SensorDynamics have partnered in developing a practical approach to intensive surveillance of remote downhole oil and gas wells and related production infrastructure. The work has resulted in a unique family of slim-line fiber optic sensors, along with a simple method of deploying these sensors remotely in oil and gas wells, using conventional hydraulic control lines as pathways into the heart of the reservoir. Most importantly the program has also produced coating technology that stabilizes the fiber optic sensors and cables in the high-temperature, high-pressure environments of deep wells. Sensors cover a wide range of parameters, including distributed temperature, pressure, acoustic sensors and acoustic sensor arrays for in-reservoir imaging of formation, fluid front movements and seismic relaxation. The result of this long-term partnership has been an integrated solution that forms a credible basis for permanent and intensive surveillance of oil and gas reservoirs, a solution that ensures availability of dynamic downhole data over the life of a reservoir. It has the additional advantage of allowing sensor up-grade, sensor replacement and the addition of new sensor types as these become available -without interruption of normal production and at minimal incremental cost. We report on a system that can address EOR applications, intelligent well monitoring and that we believe offers a basis for instrumenting deep and hot sub-sea reservoirs and sub-sea production infrastructure. We report some of the key issues in deploying sensors in hydraulic control lines in oil wells, in protecting sensors in harsh downhole environments (250deg C, 600 bar) and sensor performance results, from laboratory and field test work. We also offer some views on the implications to the future of realtime integrated reservoir management, where availability of downhole information has important value in all phases of the life of a field -from cradle to grave.
Management In information management and decision making, the "data commute" emerges repeatedly as a big problem in achieving more efficient workflows. SPE's Digital Energy Technical Section has conducted several in-depth statistical studies that show an unacceptable amount of time in the E&P workforce lost searching for data, integrating it from multiple sources, and preparing it for analysis in applications (Brulé et al. 2008; Hite et al. 2007; Mochizuki et al. 2006). Once engineering and operational data are in the application package for analysis, any updated or more recent data require a repeat of tedious, barren, and nonproductive processes to prepare and reload the data, a sort of "oilfield entropy" that is a drag on the E&P industry. The time that could have otherwise been spent making operational or project decisions is lost to activity that has nothing to do with engineering and geoscience. Some industry professionals might be resigned to dealing with data as just part of the workday, but most recognize a fundamental tenet: it is not the data per se, but decisions—based on data-derived information—that drive actions and create value(Fig. 1). We acknowledge the importance of engineering techniques, operational effectiveness, risk-factor evaluation, and project methodology, but here we separate them to examine and quantify just one assertion: making informed decisions would be quicker if the underlying necessary data are always ready to go. "If I can make that decision accurately, but a month earlier, and can do this across many projects, many wells, many fields…." Faster decisions with precision have tremendous value and provide much leverage in an industry already hindered by a shortage of qualified people. Contexts of Oilfield Integration Oilfield integration spans all areas of the oil and gas industry, from subsurface to surface to business. International oil companies (IOCs) currently have several oilfield-integration efforts as initiatives. Understanding the context of these oilfield integrations is important to understand what the IOCs are trying to accomplish. When we speak of integration, just what are we actually integrating? There exist several contexts of integration that must be recognized and accomplished to realize the vision of the digital oil field:Data integration—Data accessible to everyone, across disciplines, and consumable in softwareWorkflow and process integration—Combining and automating work processes for greater efficiencyDisciplines integration—The leverage of experts across disciplines like the geosciences, drilling, production, and reservoir (Mochizuki et al. 2006; Sankaran et al. 2009)
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