Ayoub Mohammed Mohammed Abdalla, Alakbari Fahd Saeed, Nathan Clarence Prebla, Mohyaldinn Mysara Eissa
Petroleum Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia.
ACS Omega. 2022 May 31;7(23):19735-19742. doi: 10.1021/acsomega.2c01496. eCollection 2022 Jun 14.
Determining the solution gas-oil ratio ( ) below the bubble point is a vital requirement that aids in multiple production engineering and reservoir analysis issues. Currently, there are some models available for the determination of the solution gas-oil ratio under the bubble point. However, they still may prove unreliable due to the applied assumptions and their specification to operate only under a particular range of data. In this study, the neuro-fuzzy, i.e., the adaptive neuro-fuzzy inference system (ANFIS) approach, is utilized to develop an accurate and dependable model for determining the below the bubble point pressure. A total of 376 pressure-volume-temperature datasets from Sudanese oil fields were used to establish the proposed ANFIS model. The trend analysis was applied to affirm the proper relationships between the inputs and outputs. Furthermore, using different statistical error analyses, the developed model was benchmarked against widely used empirical methods to evaluate the proposed method's performance in predicting the at pressures below the bubble point. The proposed ANFIS model performs with an average absolute percent relative error of 10.60% and a correlation coefficient of 99.04%, surpassing the previously studied correlations.
确定泡点以下的溶解气油比（ ）是一项至关重要的要求，有助于解决多个采油工程和油藏分析问题。目前，有一些模型可用于确定泡点以下的溶解气油比。然而，由于所采用的假设以及它们仅在特定数据范围内运行的局限性，这些模型可能仍然不可靠。在本研究中，采用神经模糊，即自适应神经模糊推理系统（ANFIS）方法，来开发一个准确且可靠的模型，用于确定泡点压力以下的溶解气油比。总共使用了来自苏丹油田的376个压力 - 体积 - 温度数据集来建立所提出的ANFIS模型。应用趋势分析来确认输入和输出之间的正确关系。此外，通过不同的统计误差分析，将所开发的模型与广泛使用的经验方法进行基准比较，以评估所提出方法在预测泡点以下压力下的溶解气油比时的性能。所提出的ANFIS模型的平均绝对相对误差为10.60%，相关系数为99.04%，超过了先前研究的相关性。
