Deysarkar Centre of Excellence in Petroleum Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Sci Rep. 2022 Sep 28;12(1):16154. doi: 10.1038/s41598-022-20401-6.
Commercial reservoir simulators are required to solve discretized mass-balance equations. When the reservoir becomes heterogeneous and complex, more grid blocks can be used, which requires detailed and accurate reservoir information, for e.g. porosity, permeability, and other parameters that are not always available in the field. Predicting the EUR (Estimated Ultimate Recovery) and rate decline for a single well can therefore take hours or days, making them computationally expensive and time-consuming. In contrast, decline curve models are a simpler and speedier option because they only require a few variables in the equation that can be easily gathered from the wells' current data. The well data for this study was gathered from the Montana Board of Oil and Gas Conservation's publicly accessible databases. The SEDM (Stretched Exponential Decline Model) decline curve equation variables specifically designed for unconventional reservoirs variables were correlated to the predictor parameters in a random oil field well data set. The study examined the relative influences of several well parameters. The study's novelty comes from developing an innovative machine learning (ML) (random forest (RF)) based model for fast rate-decline and EUR prediction in Bakken Shale oil wells. The successful application of this study relies highly on the availability of good quality and quantity of the dataset.
商业储层模拟器需要求解离散的质量平衡方程。当储层变得不均匀和复杂时，可以使用更多的网格块，这需要详细和准确的储层信息，例如孔隙度、渗透率和其他在现场并不总是可用的参数。因此，预测单井的 EUR（预计最终采收率）和递减率可能需要数小时或数天，这使得它们在计算上昂贵且耗时。相比之下，递减曲线模型是一种更简单、更快捷的选择，因为它们在方程中只需要几个可以从当前井数据中轻松收集的变量。本研究的井数据来自蒙大拿州石油和天然气保护委员会的公开数据库。SEDM（拉伸指数递减模型）递减曲线方程变量专门为非常规储层变量设计，与随机油田井数据集的预测参数相关联。该研究考察了几个井参数的相对影响。本研究的新颖之处在于开发了一种基于机器学习 (ML)（随机森林 (RF)）的创新模型，用于快速预测 Bakken 页岩油井的递减率和 EUR。本研究的成功应用高度依赖于数据集的质量和数量。
