Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East

• 作者: ALATRACH Yara ALNUAIMI Marwan KAVOUSI Ghahfarokhi Payam YOUSEF Alklih Mohamad
• 来源期刊: 石油勘探与开发:英文版
• 学科: 工学
• 关键词: Top-Down Modeling reservoir modeling artificial intelligence neural network data-driven model enhanced oil recovery reservoir management
• 年，卷（期）: 2020,（2）
• 页码范围: 393-399
• 页数: 7页
• 分类号: TE319

摘要

Top-Down Modeling(TDM) was developed through four main steps of data gathering and preparation, model build-up, model training and validation, and model prediction, based on more than 8 years of development and production/injection data and well tests and log data from more than 37 wells in a carbonate reservoir of onshore Middle-East. The model was used for production prediction and sensitivity analysis. The TDM involves 5 inter-connected data-driven models, and the output of one model is input for the next model. The developed TDM history matched the blind dataset with a high accuracy, it was validated spatially and applied on a temporal blind test, the results show that the developed TDM is capable of generalization when applied to new dataset and can accurately predict reservoir performance for 3 months in future. Production forecasting by the validated history matched TDM model suggest that the water production increases while oil production decreases under the given operation condition. The injection analysis of the history matched model is also examined by varying injection amounts and injection period for water and gas(WAG) process. Results reveal that higher injection volume does not necessarily translate to higher oil production in this field. Moreover, we show that a WAG process with 3 months period would result in higher oil production and lower water production and gas production than a 6 months process. The developed TDM provides a fast and robust alternative to WAG parameters, and optimizes infill well location.