Machine Learning-Based Models for Basic Sediment & Water and Sand-Cut Prediction in Matured Niger Delta Fields

Frank A. Abuh, Julius U. Akpabio, Anietie N. Okon
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Abstract

Oil production from matured fields in the Niger Delta is characterised by basic sediment and water (BS&W) and sand or sand-cut (Scut) production. The predominant factor for this production is the unconsolidated nature of the formations in the Niger Delta. The available correlations for estimating BS&W and Scut are based more on the intrinsic reservoir properties than controllable wellhead variables during oil production. This study developed neural-based models to predict BS&W and Scut based on multiple-inputs single-output (MISO) and multiple-inputs multiple-outputs (MIMO) networks using 457 datasets from 43 oilfields in the Niger Delta. The performances of the neural-based models with new fields test datasets were determined using some statistical yardsticks: coefficient of determination (R2), correlation coefficient (R), mean square error (MSE), root mean square error (RMSE), average relative error (ARE), and average absolute relative error (AARE). The results indicate that the MISO neural-based models had overall R and MSE values of 0.9999 and 2.0698\(\times\)10-5, respectively, for BS&W and 0.9995 and 2.1529\(\times\)10-6 for Scut. In contrast, the MIMO neural-based model had overall R and MSE values of 0.9997 and 7.5865\(\times\)10-5. The generalisation performance of the MISO neural-based models with new field test datasets resulted in R2, R, MSE, RMSE, ARE and AAPRE of 0.97406, 0.98695, 2.08143, 1.44272, -0.00638 and 0.28755, respectively, for the BS&W model and R2 of 0.89558, R of 0.93544, MSE of 0.01736, RMSE of 0.13177, ARE of 0.01338 and AARE of 0.01759 for the Scut model. Furthermore, the MIMO-based model with new field test datasets resulted in R2, R, MSE, RMSE, ARE and AAPRE of 0.97317, 0.98650, 2.15293, 1.46729, -0.00713 and 0.25064, respectively, for BS&W, while the Scut model had R2 of 0.87505, R of 0.93544, MSE of 0.02118, RMSE of 0.14554, ARE of -0.02280 and AARE of 0.02996. Also, the relative importance of the input parameters of the MISO and MIMO neural-based models in predicting BS&W and Scut is \(q_0\) >Pr>Pwh>S> \(\gamma\)API . Based on the statistical indicators obtained, the predictions of the developed neural models were close to the actual fields’ datasets. Thus, the neural-based models should apply as tools for estimating BS&W and Scut in mature fields in the Niger Delta.
基于机器学习的基础沉积物模型研究尼日尔三角洲成熟油田含水含砂预测
尼日尔三角洲成熟油田的产油特点是基本沉积物和水(BS&W)和砂或砂屑(Scut)生产。这种生产的主要因素是尼日尔三角洲地层的松散性。BS&W和Scut的可用相关性更多地基于储层的固有属性,而不是石油生产过程中的可控井口变量。该研究利用尼日尔三角洲43个油田的457个数据集,开发了基于多输入单输出(MISO)和多输入多输出(MIMO)网络的神经网络模型,预测BS&W和Scut。采用决定系数(R2)、相关系数(R)、均方误差(MSE)、均方根误差(RMSE)、平均相对误差(ARE)、平均绝对相对误差(AARE)等统计指标对新现场测试数据集的神经网络模型进行性能评价。结果表明,基于MISO神经网络的模型的总体R和MSE值分别为0.9999和2.0698 \(\times\) 10-5,对BS&W和Scut的R和MSE值分别为0.9995和2.1529 \(\times\) 10-6。相比之下,基于MIMO神经的模型的总体R和MSE值分别为0.9997和7.5865 \(\times\) 10-5。基于MISO神经网络的模型在新的现场测试数据集上的泛化性能表明,BS&W模型的R2、R、MSE、RMSE、ARE和AAPRE分别为0.97406、0.98695、2.08143、1.44272、-0.00638和0.28755,Scut模型的R2为0.89558、R为0.93544、MSE为0.01736、RMSE为0.13177、ARE为0.01338和AARE为0.01759。此外,基于mimo的新现场试验数据集模型对BS&W的R2、R、MSE、RMSE、ARE和AAPRE分别为0.97317、0.98650、2.15293、1.46729、-0.00713和0.25064,而Scut模型的R2为0.87505、R为0.93544、MSE为0.02118、RMSE为0.14554、ARE为-0.02280、AARE为0.02996。此外,基于MISO和MIMO神经模型的输入参数在预测BS&W和Scut时的相对重要性为\(q_0\) >Pr>Pwh>S>\(\gamma\) API。根据所获得的统计指标,所建立的神经网络模型的预测结果与现场实际数据集较为接近。因此,基于神经网络的模型可以作为估算尼日尔三角洲成熟油田BS&W和Scut的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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