Turning Data into Knowledge: Data-Driven Surveillance and Optimization in Mature Fields
- Cenk Temizel (Aera Energy) | Sinem Aktas (Turkish Petroleum) | Harnn Kirmaci (Turkish Petroleum) | Onur Susuz (Turkish Petroleum) | Ying Zhu (University of Southern California) | Karthik Balaji (University of Southern California) | Rahul Ranjith (University of Southern California) | Sofiane Tahir (ADNOC) | Fred Aminzadeh (University of Southern California) | Cengiz Yegin (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 26-28 September, Dubai, UAE
- Publication Date
- Document Type
- Conference Paper
- 2016. Society of Petroleum Engineers
- 7.2.1 Risk, Uncertainty and Risk Assessment, 7 Management and Information, 7.2 Risk Management and Decision-Making, 5 Reservoir Desciption & Dynamics
- predictive analytics, surveillance, optimization, mature fields, data-driven model
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Decision making in the oilfield is a crucial process in petroleum oilfield activities where numerous attributes and uncertainties exist in the complete process. In the development process of new fields, a well-organized historical database to minimize uncertainty is important as it decreases risks, provides better insight and robustness in decision making.
Statistics is a strong tool to turn information or data into knowledge when used with care and physical understanding of the cause-effect relation between the attributes and the outcome. Unfortunately, historical data and learnings from the past cannot be used in an efficient way in oilfield decisions due to lack of systematically organized historical data where there is a huge potential of turning terrabytes of data into knowledge and understanding, for more successful decisions and results. On the other hand, using historical data on reservoir enginnering studies are generally very complex since it requires integration of vast amount data from several diciplines that have different data sources and from different scales, which is sometimes leading modelers to prefer new data collections from the field rather than using complex historical data on hand.
In this sense, a multi-attribute based statistical model for each reservoir will greatly enhance the outcomes of future actions through bringing a statistical understanding to a physically more complex relation between the causes and effects or in other words, attributes and results. The data driven models are established for the desired phenomena by means of collecting relevant historical data, after which a multivariate regression is carried out to show the significance of each attribute in the model. These statistical models are then used to make future decisions in the same reservoir in such a way that attributes can be selected within the optimum ranges that yield best results and outcome. Attributes can consist of events and key parameters that influence the outcome.
The model is illustrated to investigate the factor affecting the performance of vertical and horizontal wells in tight reservoirs. The data-driven model is validated with the numerical reservoir simulation model and used to determine the significance of each parameter and the optimum operating intervals.
|File Size||24 MB||Number of Pages||32|
Obulesu, O., Reddy, A., & Reddy, M. (2014). Finding Frequent and Maximal Periodic Patterns in Spatiotemporal Databases towards Big Data. International Journal of Emerging Technology and Advanced Engineering, 4(1), 266-275. Retrieved from http://www.ijetae.com/files/Volume4Issuel/IJETAE_0114_48.pdf
Al-Asimi, M., Butler, G., Brown, G., Hartog, A., Clancy, T., Cosad, C., Stephenson, K. (2002). Advances in well and reservoir surveillance. Oilfield Review, 14(4), 14-35+1+65. http://doi.org/10.106371.2217920
Potters, H., & Kapteijn, P. (2005). Reservoir Surveillance and Smart Fields. International Petroleum Technology Conference, 3-5. Retrieved from http://www.onepetro.org/mslib/servlet/onepetropreview?id=IPTC-11039-MS
Intelligent Solutions Inc., 2011, Top-Down Intelligent Reservoir Models, Intelligent Solutions Inc., http://intelligentsolutionsinc.com/PDFs/WhitePaper-Top-DownModels.pdf