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.
Production Data Management and Surveillance in Shale Operations presents both the benefits and challenges of field data management in shale production operations. The examination of the lifecycle of field production data the paper offers will lead us to explore issues of surveillance, predictive analytics, enterprise data availability, data quality, integration, and regulatory compliance from the perspective of unconventional resource operators. The paper also includes case studies that detail the implementation and lessons learned from five shale oil and gas producers. Factory drilling in unconventional plays leads to highly paced operations and a large number of wells, which in turn leads to the generation of an enormous amount of field data that must all be captured, processed and turned into actionable information for surveillance, operational accounting and HSEQ compliance. This data, much of which is collected by field personnel at the well site, includes fluid volumes and operating conditions as well as run tickets, tank battery inventories, sales volumes, equipment status, and chemical usage. Also, increased sensitivity to environmental issues around shale plays requires operators to closely manage water, emissions and other environmentally impactful measurements, all of which must be collected, monitored and reported. Maintaining the quality of the gathered data is paramount as poor data can lead to costly consequences, from under- optimized production to fines for inaccurate reporting. Integrating surveillance processes with the field systems will promote consistency and accuracy. In conjunction, tools that provide asset-specific variances, alerts and visualizations will help identify operational issues immediately, allowing for swift alignment of the field team with corporate goals. Raw production measurements collected in the field are processed to produce allocated production volumes at each well and zone. These allocated volumes are then put to use by different departments across the operator’s enterprise, in their planning, forecasting, operations management, revenue accounting, marketing, regulatory and partner reporting. Field information and production data lie at the heart of operations management for all operators. Production Data Management and Surveillance in Shale Operations submits multiple case studies on the collection, analysis and distribution of this data, along with the best practices employed in shale plays.
Broad support exists in the leadership of most large oil and gas companies for a version of the digital oil field as a strategic direction, but there is difficulty at the implementation level in building a convincing case for investment. Implementation seems slow, perhaps because generating a convincing business case for incremental investment is challenging. Winning acceptance for new computing, automation, and communications technology* (CACT) in the oil patch is always a challenge, and one that begins and ends with a convincing business case. A good business case speaks to decision makers in a language they understand. It outlines in clear terms the benefits of the investment, its costs, and its risks. Each needs to be presented clearly, openly, and honestly. A good business case is guided by five principles:Value is created only when good decisions are made and implemented. The value of an oil and gas asset derives from decisions made and implemented in the past—to drill wells, to complete them, to implement recovery processes, and to install facilities. Once decisions and investments are made, the assets are operated in a way to capture the intended value. Oil fields do not 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 are all designed to make sure 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 avail-able at the time.CACT investments affect decisions. Computers do not produce oil; they facilitate decisions. Thus, the value of investments in CACT derives from its ability to improve decision making or improve on the decisions that are made. Having more terabytes of data stored on a server does not help a bit. Rather, it adds cost. Likewise, storing production rates and pressures minute by minute does not add value. Faster communications, bigger computers, and more gadgets do not help unless they lead to better or more timely decisions.Real time is defined by the frequency of decisions that need to be made. Because the value of CACT investments comes from the decisions it influences, the frequency of data collection should be matched to the rate at which decisions can be made and implemented safely. The decision rate defines what "real time" really is. Information at time scales shorter than the rate at which decisions are made does not help because the information cannot be acted on that fast. Longer time scales may mean lost opportunities because decisions could be made faster if the information were there. Thus, "real time" is really "right time"—the right time for making decisions.
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