Automated Production Surveillance
- Christian P. Oberwinkler (U. of Leoben) | David Mayfield (Murphy E&P) | Dave Dixon (NetSCADA) | Jeff Holland (W&T Offshore Inc.)
- Document ID
- Society of Petroleum Engineers
- SPE Projects, Facilities & Construction
- Publication Date
- June 2006
- Document Type
- Journal Paper
- 1 - 8
- 2006. Society of Petroleum Engineers
- 5.6.4 Drillstem/Well Testing, 5.2.1 Phase Behavior and PVT Measurements, 4.1.2 Separation and Treating, 1.6 Drilling Operations, 6.1.5 Human Resources, Competence and Training, 5.5 Reservoir Simulation, 4.4.2 SCADA, 7.1.5 Portfolio Analysis, Management and Optimization, 5.6.8 Well Performance Monitoring, Inflow Performance, 5.1.9 Four-Dimensional and Four-Component Seismic, 4.1.5 Processing Equipment, 4.6 Natural Gas, 7.6.6 Artificial Intelligence, 5.1.2 Faults and Fracture Characterisation, 2.4.3 Sand/Solids Control
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"Real time?? is the new buzz in the upstream petroleum industry. So far, operators at the lease location have been the main users of real-time data measured at second or minute increments to manage wells and keep them on production. Engineers usually see only a subset of the data—the daily production volumes and rates, along with a few selected gauge-pressure and temperature readings. The engineers' access to limited data means that they typically see only the result—the production volumes—and not the high-frequency data that may be the reason for a certain production parameter (e.g., choke size, pressures, and temperatures).
Supervisory control and data acquisition (SCADA) is the system that connects to the production facilities' controllers and data sources and collects the measured data and stores them in a database. Operators on the platform have direct access to these data and use this information to control the wells and the process equipment. If the engineers see these data at all, they usually get them through a Web-browser interface and in a format they cannot directly use for their analysis.
This paper will introduce a new concept of integrating high-frequency real-time data to the oil company's business office, making those data available to engineering staff and operations management, including up to the senior management level. Each level of the organization sees as much of the high-frequency data as it needs or wants to see. The engineers and management have exactly the same view as the operators at the platform and at the same time. This might seem to be a problem at first, but in the long term, it is an empowerment of the operators and brings engineers and operators closer together by working as a team to manage the wells. The data also allow management to monitor the oil and gas production leaving the platform to see if the target business plan volumes are reached or if a well is shut in.
This paper will give insights on how the access to high-frequency data changed the way of doing the daily work and how it changed the way operators work together with engineers. (Note: All values in the figures within this paper are manipulated and do not necessarily represent reality).
Horizontal wells and 4D seismic have been the last major technological advances that the upstream petroleum industry. Now, it appears as the so-called "intelligent field?? (also named Smart Field, e-Field, and i-Field) will be the next major technological advance in the industry. But how is an intelligent field defined? Phrases such as "closed loop?? and "self-controlled?? can be read in different publications. This is not what we will focus on in this paper, because closed-loop control still has a long way to go before it becomes reality. We will focus on the basics for an intelligent field, starting with the following questions:
1. What are the main problems?
2.What has to be done on the data-management side?
3.How can high-frequency data add value to the asset-management process?
As the water depth of newly discovered reservoirs is getting deeper and deeper, the costs of drilling a well have sharply increased. A deepwater well can typically cost U.S. $15 to $50 million. The facility to support these wells may cost an additional U.S. $200 to $1,000 million. These costs have necessitated a higher level of well-performance monitoring to protect these investments. The wells and the platform are equipped with a variety of different sensors, measuring the performance of the wells and the platform's process trains with seconds-to-minute increments.
|File Size||2 MB||Number of Pages||8|
de Jonge, G., Stundner, M., and Zangl, G. 2003. Automated Reservoir SurveillanceThrough Data Mining Software. Paper SPE 83974 presented at the SPE OffshoreEurope Conference, Aberdeen, 2-5 September.
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