Dynamic Layered Pressure Map Generation in a Mature Waterflooding Reservoir Using Artificial Intelligence Approach
- Yuanjun Li (University of Southern California) | Andrei Popa (Chevron) | Andrew Johnson (Chevron) | Iraj Ershaghi (University of Southern California) | Steve Cassidy (Chevron)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 22-26 April, Garden Grove, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 5.4.1 Waterflooding, 7.6 Information Management and Systems, 5.4 Improved and Enhanced Recovery, 7 Management and Information, 5 Reservoir Desciption & Dynamics, 7.6.6 Artificial Intelligence, 7.6.7 Neural Networks
- neural networks, reservoir pressure, Waterflooding, fall-off tests, fuzzy-kriging
- 1 in the last 30 days
- 236 since 2007
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In this paper, we present an Artificial Neural Network (ANN) solution approach to estimate average reservoir pressure from partial data recorded from fall-off tests. The methodology, while relatively simple, is entirely based on calibrating data from well tests with full fall-off information. Compared with conventional reservoir pressure estimation models, which usually require a long shut-in time and manual analysis for an individual well, this approach provides an efficient way of processing large data sets. Using data from injection well fall-off tests can avoid cost from non-producing periods of production wells. Also, utilizing injection profile tracers with shut-in pressure trends at the surface, we can infer the sand face pressure trends assuming a hydrostatic column during a shut-in period, greatly reducing instrumentation costs and downhole jewelry requirements.
This methodology is extremely helpful for mapping average pressure around injection wells in a reservoir with hundreds of injection wells with multiple flow units. In principle, maps generated from fall-off tests can be used as diagnostic tools to assist in formulating water flooding plans, ameliorating oil production capability, arranging well pattern density and preserving reservoir stability by providing an overview of internal oilfield conditions. Because of its practical simplicity and maneuverability, this new methodology can rapidly offer actionable data and generate remarkable business value.
|File Size||1 MB||Number of Pages||14|
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