zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-1867Research ArticleАННОТАЦИИ АНГЛОЯЗЫЧНЫХ СТАТЕЙABSTRACTS ENGLISH-LANGUAGE ARTICLESПрогнозирование содержания нефти в воде, закачиваемой на нефтяные месторождения, на основе моделирования ResNetOil Content Prediction of Oilfield Reinjection Water Based on Residual Neural NetworksZhangX.ZhangX.

Дацин

Xiaoxue Zhang

Daqing

qihanbing1975@126.comLiangS.LiangS.

Дацин

Shujuan Liang

Daqing

ZhuH.ZhuH.

Дацин

Hang Zhu

Daqing

LiH.LiH.

Дацин

Huaizhi Li

Daqing

QiH.QiH.

Дацин

Hanbing Qi

Daqing

TianB.TianB.

Чаоян

Boyu Tian

Chaoyang

Ключевая лаборатория провинции Хэйлунцзян по использованию тепловой энергии и уменьшению опасности стихийных бедствий в холодных регионах; Школа архитектуры и гражданского строительства Северо-Восточного нефтяного университета; Международная лаборатория по взаимодействию низкоуглеродной и новой энергетики Северо-Восточного нефтяного университетаHeilongjiang Provincial Key Laboratory of Thermal Utilization and Disaster Reduction of New Energy in Cold Regions; School of Architecture and Civil Engineering, Northeast Petroleum University; International Joint Laboratory on Low-Carbon and New-Energy Nexus, Northeast Petroleum UniversityГосударственная трубопроводная сеть “Пекинская трубопроводная компания”State Pipe Network Group Beijing Pipeline Company202520052025923407407Copyright © Zhang X., Liang S., Zhu H., Li H., Qi H., Tian B., 20252025Zhang X., Liang S., Zhu H., Li H., Qi H., Tian B.Zhang X., Liang S., Zhu H., Li H., Qi H., Tian B.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://zhps.ejournal.by/jour/article/view/1867

Представлены быстрые и точные методы прогнозирования содержания нефти в воде, повторно закачиваемой на нефтяные месторождения. На станции очистки сточных вод месторождения Дацин отобрано 146 образцов воды для повторного использования. Для прогнозирования содержания нефти на основе спектров пропускания в УФ-видимом диапазоне 190—900 нм построены три модели остаточной нейронной сети (ResNet) с различными сетевыми структурами и несколькими слоями. Сравнительный анализ проведен методом частичных наименьших квадратов siPLS. Средние абсолютные ошибки трех моделей ResNet (1.23, 0.76 и 0.29 мг/л) демонстрируют более низкие значения по сравнению с полученными методом siPLS, в частности, увеличение количества слоев в модели ResNet повышает точность обнаружения. Продемонстрирована пригодность модели ResNet для прогнозирования содержания нефтепродуктов в сточных водах около 20.0 мг/л, как предписано отраслевыми спецификациями.

Predicting the oil content of reinjection water is a crucial challenge in the advancement of digital oilfield technologies. To effectively address this challenge, this study investigates rapid and accurate prediction methods for oil content in reinjection water. A total of 146 samples of reinjection water were collected from a sewage treatment station in the Daqing oilfield. Using UV-visible transmission spectra ranging from 190 to 900 nm, three residual neural networks (ResNet) models with different network structures and several layers were constructed for predicting oil content. Comparative analysis was performed using the joint interval partial least squares method (siPLS). Results showed that the mean absolute errors of the three ResNet models were 1.23, 0.76, and 0.29 mg/L, respectively, all demonstrating lower values than those obtained with the siPLS model, notably, increasing the number of layers in the ResNet model enhanced detection accuracy. Consequently, the ResNet model proves to be suitable for predicting oily sewage content within the 20.0 mg/L range as mandated by industry specifications.

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The authors declare that there are no conflicts of interest present.