Alakbari Fahd Saeed, Mohyaldinn Mysara Eissa, Ayoub Mohammed Abdalla, Muhsan Ali Samer, Hussein Ibnelwaleed A
Petroleum Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
Mechanical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
PLoS One. 2021 Apr 26;16(4):e0250466. doi: 10.1371/journal.pone.0250466. eCollection 2021.
Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model's reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.
砂管理对于提高油气藏产量至关重要。临界总压降（CTD）被用作出砂开始的可靠指标；因此，其准确预测非常重要。文献中有许多已发表的CTD预测关联式。然而，这些模型大多的准确性值得怀疑。因此，为了更有效和成功地进行防砂，需要进一步改进CTD预测。本文提出了一种用于预测CTD的强大且准确的模糊逻辑（FL）模型。利用亚得里亚海北部23口井的文献数据来开发该模型。所使用的数据被分为70%的训练集和30%的测试集。进行趋势分析以验证所开发的模型是否遵循输入参数的正确物理行为趋势。与已发表的关联式相比，进行了一些统计分析以检验模型的可靠性和准确性。结果表明，所提出的FL模型明显优于当前已发表的关联式，且显示出更高的预测准确性。使用最高相关系数、最低平均绝对百分比相对误差（AAPRE）、最低最大误差（max. AAPRE）、最低标准差（SD）和最低均方根误差（RMSE）对这些结果进行了验证。结果表明，最低AAPRE为8.6%，而最高相关系数为0.9947。这些AAPRE值（<10%）表明，FL模型比其他已发表的模型（AAPRE>20%）能更准确地预测CTD。此外，进一步分析表明了FL模型的稳健性，因为它遵循了所有影响CTD的物理参数的趋势。
