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Oil production monitoring and optimization from produced water analytics; a case study from the Halfdan chalk oil field, Danish North Sea

机译：通过采出水分析进行石油生产监控和优化;丹麦北海哈夫丹白垩油田的案例研究

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Produced water analysis is a direct source of information to the subsurface processes active in an oil field. The information is, however, complex and requires a multidisciplinary approach and access to multiple data types and sources to successfully unlock and decode the processes. We apply data analytics on a combined data set of water chemistry and oil and gas production data measured in the production stream from five wells in the Halfdan field. The field is produced applying extensive water injection to ensure the most efficient water sweep of the reservoir. Relationships between daily production data and water chemistry are examined with Principal Component Analysis (PCA), and systematics with respect to predictability of daily changes in the oil production from water chemistry are examined with partial least square (PLS) regression models. For each well, the water chemistry provides a high degree of predictability with respect to daily oil cut in the production stream. The results have potential for application within prediction of sweep efficiency, by-passed oil and for prediction of water break-through. Full potential, however, depend on successful implementation of water chemistry-oil production analytics into other data domains such as seismic (4D) data and well work-over data.
机译：采出水分析是在油田活跃的地下过程的直接信息来源。但是，该信息非常复杂，需要多学科的方法，并且需要访问多种数据类型和源才能成功解锁和解码过程。我们将数据分析应用于水化学和油气产量数据的组合数据集，该数据是在Halfdan油田的5口井的生产流中测得的。该油田是通过大量注水​​生产的，以确保对储层进行最有效的注水。日产量数据与水化学之间的关系通过主成分分析（PCA）进行了检验，并且通过偏最小二乘（PLS）回归模型对水化学日产量变化的可预测性进行了系统分析。对于每口井，相对于生产流中的每日石油削减，水的化学性质提供了高度的可预测性。该结果可用于预测波及效率，旁路油和预测水突破。但是，全部潜力取决于成功将水化学-石油生产分析应用于其他数据领域，例如地震（4D）数据和修井数据。

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