Abstract
In this paper, we present how precise deep learning algorithms can distinguish loss circulation severities in oil drilling operations. Lost circulation is one of the costliest downhole problem encountered during oil and gas well construction. Applying artificial intelligence can help drilling teams to be forewarned of pending lost circulation events and thereby mitigate their consequences. Data-driven methods are traditionally employed for fluid loss complexity quantification but are not able to achieve reliable predictions for field cases with large quantities of data. This paper attempts to investigate the performance of deep learning (DL) approach in classification the types of fluid loss from a very large field dataset. Three DL classification models are evaluated: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). Five fluid-loss classes are considered: No Loss, Seepage, Partial, Severe, and Complete Loss. 20 wells drilled into the giant Azadegan oil field (Iran) provide 65,376 data records are used to predict the fluid loss classes. The results obtained, based on multiple statistical performance measures, identify the CNN model as achieving superior performance (98% accuracy) compared to the LSTM and GRU models (94% accuracy). Confusion matrices provide further insight to the prediction accuracies achieved. The three DL models evaluated were all able to classify different types of lost circulation events with reasonable prediction accuracy. Future work is required to evaluate the performance of the DL approach proposed with additional large datasets. The proposed method helps drilling teams deal with lost circulation events efficiently.Article HighlightsThree deep learning models classify fluid loss severity in an oil field carbonate reservoir.Deep learning algorithms advance machine learning a large resource dataset with 65,376 data records.Convolution neural network outperformed other deep learning methods.
Highlights
Drilling oil and gas wells is associated with a variety of downhole problems
The global cost associated with lost circulation is estimated to be hundreds of million dollars, which includes lost drilling fluids and cost of treatments, as well as the lost drilling time and cost of specialist tools and materials involved in its remediation
In order to compare the performance of Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) models, the number of epochs executed was set equal for all models
Summary
Drilling oil and gas wells is associated with a variety of downhole problems. The cost of lost drilling fluid and treatment of circulating system is a burden for the drilling industry. Loss circulation occurs downhole usually associated with two distinct conditions: 1) the wellbore pressure exceeds the pore pressure of the subsurface formations, or, 2) a large subsurface void space (pore or fracture related) is encountered that consumes wellbore fluids [2, 3]. The void spaces are extensively distributed throughout the formations being drilled such that that even underbalanced systems can lead to mud losses This typically occurs because natural fractures do not have sufficient strength to bear the well fluid pressure resulting in tensile failure. Implementing statistical methods using actual operational data can help to mitigate potential risks of drilling fluid losses from occurring Such methods attempt to predict the subsurface zones susceptible to lost circulation and classify them in terms of the loss severity most likely to occur.
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