Benchmarking Measurement and Automation Practices in the Upstream Business
- Bill V. Liddell (Anadarko Petroleum Corp.) | David Frank Deaton (Halliburton) | Gerardo Mijares (Halliburton)
- Document ID
- Society of Petroleum Engineers
- SPE Projects, Facilities & Construction
- Publication Date
- June 2007
- Document Type
- Journal Paper
- 1 - 7
- 2007. Society of Petroleum Engineers
- 4.3.1 Hydrates, 4.3.4 Scale, 5.6.9 Production Forecasting, 3.1.5 Plunger lift, 7.6.1 Knowledge Management, 7.6.6 Artificial Intelligence, 7.6.2 Data Integration, 3.1.2 Electric Submersible Pumps, 4.4.2 SCADA, 7.3.3 Project Management, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 7.7.1 New Technology Deployment, 4.1.1 Process Simulation, 6.1.5 Human Resources, Competence and Training
- 1 in the last 30 days
- 473 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Whether we call it "digital energy,?? "digital oil field of the future,?? "real-time operations,?? "intelligent fields,?? "fields of the future,?? or "e-fields,?? there is a clear incentive to use technology to maximize asset operational value through increased production and improved efficiencies. The application of visualization, modeling, optimization, and control technologies is a critical component of this effort. This paper describes a study to determine current industry practices and establish a benchmark in the production-measurement and automation (PMA) area. Using a proven analytic methodology designed specifically to survey and analyze technology use, 125 questions were grouped into 17 categories addressing personnel, process, and technology aspects of PMA. The presented results are from 10 leading independent oil- and gas-producing organizations.
Interviews were conducted to clarify the participants' responses, and each response was then reviewed and scored on the basis of pre-established criteria, consulting experience, and previous assessment results. Participants' scores were compared to industry averages and the scores of the PMA leaders by use of bar and radar charts. Questions included qualitative topics, such as the participants' automation-team organizational model, as well as quantitative questions such as the number of engineers per well and the number of historian database points.
This comparison of practices between peer-group members participating in the benchmarking process uncovered many significant areas of opportunity for the participants. The results were as expected in some categories and surprising in others. In general, there is a low level of use of PMA, combined with a keen interest in doing more than is being done currently. A few companies exhibited high application of automation, with successful results, by leveraging organizations that promote strategic adoption of technology.
This study provides guidance to oil- and gas-producing companies desiring to increase their productivity through PMA projects or those desiring to increase their return on existing PMA projects. The benchmarking methodology described in this paper is part of a process that uses the results of participants' current application of technology along with an assessment of the value of opportunities to determine the best path to take toward technology implementation, including strategies and project roadmaps. Studies by independent organizations such as Cambridge Energy Research Associates (CERA) point out quantifiable benefits from the application of digital technology in terms of the percentage of reserves-recovery increase, the percentage of production increase, and the percentage reduction in operating costs (Cambridge Energy Research Associates 2005). The effort presented focused specifically on improvements enabled by better measurement, modeling, and control technologies. This study also focuses on how the successful implementers of technology are managing the implementation and post-implementation process to assure the capture of the expected benefits.
|File Size||614 KB||Number of Pages||7|
Bruni, T., Lentini, A., Ventura, S.,Gheller, R., Maybee, C,, and Pinedo,J. 2003. A Technically Rigorousand Fully Automated System for Performance Monitoring and Production TestValidation. SPE Paper 84881 presented at the SPE InternationalImproved Oil Recovery Conference in Asia Pacific, Kuala Lumpur, 20-21 October.DOI: 10.2118/84881-MS.
Cambridge Energy Research Associates.2005. Making the Leap Towards DOF Adoption. White Paper, January.
Cambridge Energy Research Associates.2005. Strategic Value and the Digital Oilfield of the Future. White Paper,January.
Mochizuki, S., et al. 2004. Real Time Optimization:Classification and Assessment. SPEPO 21 (4): 455-466.SPE-90213-PA. DOI: 10.2118/90213-PA.
Moore, Geoffrey A. 2002. Crossing theChasm. New York City: Harper Collins Publishers.