Abstract The methods used in allocating commingled production in conventional reservoirs are similar to those that are effective in performing the same task on unconventional reservoirs. However, the protocols to follow can vary a great deal. The presence of distinct endmembers in the former allows the use of the method of production allocation using peak height ratios and mixing curves and using linear regression of peak heights. Due to the possible contribution from multiple intervals in the same formation or even from different formations as in the case of fracture stimulated unconventional reservoirs, the combined application of methods that compare the quantities and carbon stable isotopes of selected compounds (such as saturate and aromatic hydrocarbons) and other parameters (such as API gravity) was employed. This was done based on a series of samples presumed to represent the endmembers via their HRGC and GCMS oil fingerprint, followed by the determination of the contribution from each sample by using an algebraic solution of simultaneous linear equations. A review of the two methods is provided. The aforementioned method for unconventional resources is demonstrated in a case study of production allocation that was performed on three produced oils sampled at different times from three separate wells, “A” “C”, and “D”, located in the Western Canadian Sedimentary Basin. A total of 25 core extract samples representing two producing zones (end members) of the Montney Formation (i.e., the Middle and the Lower Montney) from well “A” and “B” were used. Results of GC and GCMS analyses of the samples were evaluated; rigorous filters, cluster analysis (dendrograms), and Principal Component Analysis (PCA) were applied to identify any clustering or variation between the samples representing possible contributor layers and the commingled oil. Then, using proprietary software and statistical techniques, the fingerprint of selected compounds was qualitatively compared and their quantity in each of the rock extracts and the produced oils was determined in order to allocate the contribution from the two end members that each extract belongs to. Results from the method for unconventional resources were compared to other data (such as GC trace patterns) for consistency. The case study demonstrates that a combined approach that accounts for the entire fingerprint (i.e., GC and molecular markers (including biomarker and non-biomarker parameters)), produces the best results and minimizes uncertainty.

中文翻译：

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使用地球化学指纹技术在非常规储层中混合生产的井后增产分配：方法回顾和加拿大西部沉积盆地 Montney 组的案例研究

摘要 常规油藏混合生产分配的方法与在非常规油藏上执行相同任务的有效方法相似。但是，要遵循的协议可能会有很大差异。前者中不同端元的存在允许使用使用峰高比和混合曲线以及使用峰高线性回归的生产分配方法。由于同一地层中的多个层段甚至不同地层的可能贡献，如在压裂增产的非常规储层的情况下，比较选定化合物（例如饱和烃和芳烃）的数量和碳稳定同位素的方法的组合应用) 和其他参数（例如 API 重力）。这是基于一系列假设通过 HRGC 和 GCMS 油指纹代表端元的样品完成的，然后通过使用联立线性方程的代数解来确定每个样品的贡献。提供了对这两种方法的回顾。上述非常规资源的方法在一个生产分配案例研究中得到了证明，该案例研究对从位于加拿大西部沉积盆地的三个独立井“A”、“C”和“D”在不同时间采样的三种采出油进行. 使用了来自“A”和“B”井的总共 25 个岩心提取样品，代表了 Montney 地层（即中和下 Montney）的两个生产区（末端成员）。评估了样品的 GC 和 GCMS 分析结果；严格的过滤器，聚类分析（树状图）和主成分分析 (PCA) 用于识别代表可能的贡献层和混合油的样品之间的任何聚类或变化。然后，使用专有软件和统计技术，对所选化合物的指纹进行定性比较，并确定它们在每种岩石提取物和生产的油中的数量，以分配每种提取物所属的两个末端成员的贡献。将非常规资源方法的结果与其他数据（例如 GC 跟踪模式）进行比较，以保持一致性。案例研究表明，考虑整个指纹（即 GC 和分子标记（包括生物标记和非生物标记参数））的组合方法，