Intelligent Production Modeling Using Full Field Pattern Recognition
- Yasaman Khazaeni (West Virginia University) | Shahab D. Mohaghegh (West Virginia University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- December 2011
- Document Type
- Journal Paper
- 735 - 749
- 2011. Society of Petroleum Engineers
- 5.4.1 Waterflooding, 5.6.1 Open hole/cased hole log analysis, 5.6.9 Production Forecasting, 6.1.5 Human Resources, Competence and Training, 7.6.6 Artificial Intelligence
- Production Data Analysis, Artificial Intelligence, Reservoir Simulation, Mature Fields
- 3 in the last 30 days
- 799 since 2007
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Production-data analysis has been applied extensively to predicting future production performance and field recovery. These applications operate mostly on a single-well basis. This paper presents a new approach to production-data analysis using artificial-intelligence (AI) techniques in which production history is used to build a fieldwide performance-prediction model. In this work, AI and data-driven modeling are used to predict future production of both synthetic- (for validation purposes) and real-field cases.
In the approach presented in this article, production history is paired with field geological information to build data sets containing the spatio-temporal dependencies among different wells. These dependencies are addressed by compiling information from closest offset wells (COWs) that includes their geological and reservoir characteristics (spatial data) as well as their production history (temporal data).
Once the data set is assembled, a series of neural networks are trained using a back-propagation algorithm. These networks are then fused together to form the intelligent time-successive production-modeling (ITSPM) system. This technique uses only the widely available measured data such as well logs and production history of existing wells to predict future performance of the existing wells and production performance of the new (infill) wells. To demonstrate the applicability of this method, a synthetic oil reservoir is modeled using a commercial simulator. Production and well-log data are extracted into an all-inclusive data set. Several neural networks are trained and validated to predict different stages of the production. The ITSPM method is used to estimate the production profile for nine new wells in the reservoir. Furthermore, ITSPM is also applied to two giant oil fields in the Middle East. The first one has more than 200 wells and 40 years of production history. ITSPM?s production predictions of the four newest wells in this reservoir are compared with their actual production. The second real field has hundreds of wells producing from multiple layers. The field has undergone waterflooding for almost its whole life. This case also shows the capabilities of this technique in more-complex scenarios and especially multiphase systems.
|File Size||9 MB||Number of Pages||15|
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