A typical Recurrent Neural Networks include Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Simple RNN has been developed to predict oil and gas production. The results have shown that machine learning gained good results in the early stage of the production phase and the ...
This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventi...
to forecast real field oil production.A pre-process step performed to reduce noise and select optimal data for HONN model.The higher-order synaptic operations (HOSO) were employed to train the HONN models.HONN model tested on two case studies by applying oil, gas, and water production data...
Fig. 3. Forecast for world oil demand and conventional oil supply capacity in future. Download: Download high-res image (258KB) Download: Download full-size image Fig. 4. Comparison of production from different types of reservoirs according to 3 recovery methods. Today, various EOR methods have...
Machine Learning in Oil and Gas Blog Expanding Machine Learning Models for Better Production Forecasting in Emerging Shale Basins In this blog post, we use machine learning to test a data-limited model in the Northwest Read more byKiran Sathaye ...
forecast=n.预测,预报 vt.预示 forehead=n.额头,前部 foreign=a.外国的;外来的 foreigner=n.外国人 foremost=a.最初的;第一流的 forest=n.森林;森林地带 forever=ad.永远,总是,老是 forget=vt.忘记,遗忘 forgive=vt.原谅,饶恕,宽恕 fork=n.餐叉;叉;分叉 form=n.形式;形状 vt.形成 formal=a.正式的;...
1. A method for optimizing exploration, production and gathering from at least one well of oil and natural gas fields using a petroleum analytics learning machine system to maximize production while minimizing costs, comprising the steps of: collecting structured digital data and unstructured textual ...
In this study, time series forecast models utilizing robust and efficient machine learning techniques are formulated for the prediction of production. We have fused the two-stage data preprocessing methods and the attention mechanism into the temporal convolutional network-gated recurrent unit (TCN-GRU)...
The proposed machine learning model uses monthly production data as features. The sliding window technique is utilized to cover all production months. The model applies the Arps differencing technique to reduce the variation in the statistical properties that make the series non-stationary. Moreover, ...
The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhan